Relevant bibliographies by topics / Odometri

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Odometri'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Odometri"

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Rahman, Abdul, Mohammad Zaenal Arifin, Gesit Pratiknyo, and Bagus Irawan. "DESIGN OF MECHANISM AND MOTION SYSTEM ON TANK PROTOTYPE USING ODOMETRY." JOURNAL ASRO 11, no. 03 (August 31, 2020): 10. http://dx.doi.org/10.37875/asro.v11i03.303.

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At present, Indonesia's defense forces are not yet on par with European countries. The size is the variation of Indonesia's defense equipment technology that has not been able to keep up with the defense equipment of European countries . Utilization of unmanned tank rides is a solution in empowering mobile combat equipment as a means of national defense and defense . Unarmed armored tank defense equipment as a means of national defense and defense . The Odometry technique applied in this study uses an encoder sensor device mounted on the two right and left wheels of the prototype tank. Wheel movement will produce the number of turns that can be calculated using an encoder sensor . the authors use odometry techniques aimed at estimating distances to destinations within a relatively small measurement area, in the wider measuring area this study uses GPS. Another goal is to get the accuracy and accuracy of distance measurements against the destination location . The results obtained in this study are the level of accuracy of the measurement of distance traveled in units of cm from the results of testing and implementation of the odometry system . The application of odometry is used to get the calculation of the change in distance due to the displacement of the prototype location during the autopilot motion process in relation to getting the accuracy of moving the position towards the waypoint . The measurement error rate obtained is less than 1%. It is expected that this system can be used as a motion mechanism system on alutsi s ta tank. Keywords : Odometri, Encoder , Waypoint, Autopilot
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Asrofi, Anan, Achmad Komarudin, and Agus Pracoyo. "Navigasi Robot Mobil 3wd Omni-wheeled dengan Metode Odometri." Jurnal Elektronika dan Otomasi Industri 1, no. 1 (March 5, 2020): 44. http://dx.doi.org/10.33795/elkolind.v1i1.33.

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Makalah ini menyajikan tentang model pergerakanrobot mobil. Tantangan yang dihadapi bagaimana robot dapatbernavigasi dan mengetahui dimana posisi robot saat bergerakdengan sudut tertentu untuk mencapai suatu titik target. Robotmenggunakan system penggerak differential dengan sensor ro-tary encoder yang diolah dengan metode odometry sehinggamenghasilkan titik koordinat. Robot nantinya bergerak padaposisi awal (0,0) menuju ke titik target (x’,y’), untuk menujuke titik target robot ini di kontrol dengan metode odometrydengan memadukan metode PID. Hasil yang didapat denganmenentukan titik target dengan kecepatan 50 (pwm) menunjukanbahwa robot dapat mengikuti jalur yang dibuat dengan simpan-gan terjauh sebesar 8cm pada sumbu x negatif sampai 7cm padasumbu x positif serta 25cm pada sumbu y negatif dan 16cm padasumbu y positif
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Srinivasan, M., S. Zhang, and N. Bidwell. "Visually mediated odometry in honeybees." Journal of Experimental Biology 200, no. 19 (October 1, 1997): 2513–22. http://dx.doi.org/10.1242/jeb.200.19.2513.

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The ability of honeybees to gauge the distances of short flights was investigated under controlled laboratory conditions where a variety of potential odometric cues such as flight duration, energy consumption, image motion, airspeed, inertial navigation and landmarks were manipulated. Our findings indicate that honeybees can indeed measure short distances travelled and that they do so solely by analysis of image motion. Visual odometry seems to rely primarily on the motion that is sensed by the lateral regions of the visual field. Computation of distance flown is re-commenced whenever a prominent landmark is encountered en route. 'Re-setting' the odometer (or starting a new one) at each landmark facilitates accurate long-range navigation by preventing excessive accumulation of odometric errors. Distance appears to be learnt on the way to the food source and not on the way back.
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Yang, Jingdong, Jinghui Yang, and Zesu Cai. "An efficient approach to pose tracking based on odometric error modelling for mobile robots." Robotica 33, no. 6 (April 1, 2014): 1231–49. http://dx.doi.org/10.1017/s0263574714000654.

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SUMMARYOdometric error modelling for mobile robots is the basis of pose tracking. Without bounds the odometric accumulative error decreases localisation precision after long-range movement, which is often not capable of being compensated for in real time. Therefore, an efficient approach to odometric error modelling is proposed in regard to different drive type mobile robots. This method presents a hypothesis that the motion path approximates a circular arc. The approximate functional expressions between the control input of odometry and non-systematic error as well as systematic error derived from odometric error propagation law. Further an efficient algorithm of pose tracking is proposed for mobile robots, which is able to compensate for the non-systematic and systematic error in real time. These experiments denote that the odometric error modelling reduces the accumulative error of odometry efficiently and improves the specific localisation process significantly during autonomous navigation.
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Nevalainen, Paavo, Qingqing Li, Timo Melkas, Kirsi Riekki, Tomi Westerlund, and Jukka Heikkonen. "Navigation and Mapping in Forest Environment Using Sparse Point Clouds." Remote Sensing 12, no. 24 (December 14, 2020): 4088. http://dx.doi.org/10.3390/rs12244088.

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Odometry during forest operations is demanding, involving limited field of vision (FOV), back-and-forth work cycle movements, and occasional close obstacles, which create problems for state-of-the-art systems. We propose a two-phase on-board process, where tree stem registration produces a sparse point cloud (PC) which is then used for simultaneous location and mapping (SLAM). A field test was carried out using a harvester with a laser scanner and a global navigation satellite system (GNSS) performing forest thinning over a 520 m strip route. Two SLAM methods are used: The proposed sparse SLAM (sSLAM) and a standard method, LeGO-LOAM (LLOAM). A generic SLAM post-processing method is presented, which improves the odometric accuracy with a small additional processing cost. The sSLAM method uses only tree stem centers, reducing the allocated memory to approximately 1% of the total PC size. Odometry and mapping comparisons between sSLAM and LLOAM are presented. Both methods show 85% agreement in registration within 15 m of the strip road and odometric accuracy of 0.5 m per 100 m. Accuracy is evaluated by comparing the harvester location derived through odometry to locations collected by a GNSS receiver mounted on the harvester.
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Sun, Qian, Ming Diao, Yibing Li, and Ya Zhang. "An improved binocular visual odometry algorithm based on the Random Sample Consensus in visual navigation systems." Industrial Robot: An International Journal 44, no. 4 (June 19, 2017): 542–51. http://dx.doi.org/10.1108/ir-11-2016-0280.

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Purpose The purpose of this paper is to propose a binocular visual odometry algorithm based on the Random Sample Consensus (RANSAC) in visual navigation systems. Design/methodology/approach The authors propose a novel binocular visual odometry algorithm based on features from accelerated segment test (FAST) extractor and an improved matching method based on the RANSAC. Firstly, features are detected by utilizing the FAST extractor. Secondly, the detected features are roughly matched by utilizing the distance ration of the nearest neighbor and the second nearest neighbor. Finally, wrong matched feature pairs are removed by using the RANSAC method to reduce the interference of error matchings. Findings The performance of this new algorithm has been examined by an actual experiment data. The results shown that not only the robustness of feature detection and matching can be enhanced but also the positioning error can be significantly reduced by utilizing this novel binocular visual odometry algorithm. The feasibility and effectiveness of the proposed matching method and the improved binocular visual odometry algorithm were also verified in this paper. Practical implications This paper presents an improved binocular visual odometry algorithm which has been tested by real data. This algorithm can be used for outdoor vehicle navigation. Originality/value A binocular visual odometer algorithm based on FAST extractor and RANSAC methods is proposed to improve the positioning accuracy and robustness. Experiment results have verified the effectiveness of the present visual odometer algorithm.
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Tang, Hengbo, and Yunhui Liu. "Automatic Simultaneous Extrinsic-Odometric Calibration for Camera-Odometry System." IEEE Sensors Journal 18, no. 1 (January 1, 2018): 348–55. http://dx.doi.org/10.1109/jsen.2017.2764125.

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Al Fadli, Muhammad Hanifudin, Munawar Agus Riyadi, and Budi Setiyono. "PERANCANGAN SISTEM ROKET KENDALI BERPEMANDU INFRAMERAH MENGGUNAKAN METODE PENGOLAHAN CITRA YANG DISIMULASIKAN DALAM TEROWONGAN ANGIN." TRANSIENT 7, no. 1 (March 13, 2018): 152. http://dx.doi.org/10.14710/transient.7.1.152-159.

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Roket kendali adalah sistem senjata berbasis roket yang memiliki pengendalian otomatis untuk mencari target dan menyesuaikan arah terbangnya. Pada Penelitian ini dirancang sebuah prototipe sistem roket kendali dengan sensor pelacakan sasaran inframerah menggunakan kamera webcam yang dimodifikasi dengan penggantian lensa bawaan dengan lensa tapis pelewat sempit 940 nm. Kamera tersebut diakses menggunakan Raspberry Pi untuk selanjutnya dilakukan proses penapisan citra menggunakan metode pengambangan parameter HSV. Mikrokontroler atmega328 dipasang untuk mengendalikan pergerakan 4 buah servo canard menggunakan metode kendali PID. Dilakukan pula pengambilan parameter IMU 9-DOF dari sensor giroskop dan akselerometer MPU-6050 serta kompas HMC5883l untuk ditampilkan dalam antarmuka C#. Parameter data pelacakan sasaran dan IMU dikirimkan ke komputer menggunakan modul telemetri APC220. Sistem roket kendali yang dirancang kemudian disimulasikan gerakannya dalam terowongan angin. Keluaran dari penelitian ini menghasilkan prototype sistem roket kendali dengan instrumen pelacakan inframerah yang mampu melacak sasaran inframerah 940 nm dengan kecepatan pelacakan sebesar 49,81 FPS. Parameter pengambangan HSV untuk sasaran inframerah 940 nm bernilai hue 0-153, saturation 0-32 dan value 179-255. Parameter PID yang digunakan dalam simulasi dengan kecepatan angin 9±4 m/s bernilai kp = 7, ki = 0, dan kd = 50. Data dari telemetri dapat ditampilkan dalam odometri 2D menggunakan C#.
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Youssef, Ahmed A., Naif Al-Subaie, Naser El-Sheimy, and Mohamed Elhabiby. "Accelerometer-Based Wheel Odometer for Kinematics Determination." Sensors 21, no. 4 (February 13, 2021): 1327. http://dx.doi.org/10.3390/s21041327.

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Various high budget industries that utilize wheel-based vehicles rely on wheel odometry as an integral aspect of their navigation process. This research introduces a low-cost alternative for typical wheel encoders that are typically used to determine the on-track speed of vehicles. The proposed system is referred to as an Accelerometer-based Wheel Odometer for Kinematics determination (AWOK). The AWOK system comprises just a single axis accelerometer mounted radially at the center of any given wheel. The AWOK system can provide direct distances instead of just velocities, which are provided by typical wheel speedometers. Hence, the AWOK system is advantageous in comparison to typical wheel odometers. Besides, the AWOK system comprises a simple assembly with a highly efficient data processing algorithm. Additionally, the AWOK system provides a high capacity to handle high dynamics in comparison to similar approaches found in previous related work. Furthermore, the AWOK system is not affected by the inherited stochastic errors in micro-machined electro-mechanical systems (MEMS) inertial sensors, whether short-term or long-term errors. Above all, the AWOK system reported a relative accuracy of 0.15% in determining the distance covered by a car.
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Nikitenko, Agris, Aleksis Liekna, Martins Ekmanis, Guntis Kulikovskis, and Ilze Andersone. "Single Robot Localisation Approach for Indoor Robotic Systems through Integration of Odometry and Artificial Landmarks." Applied Computer Systems 14, no. 1 (June 1, 2013): 50–58. http://dx.doi.org/10.2478/acss-2013-0006.

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Abstract we present an integrated approach for robot localization that allows to integrate for the artificial landmark localization data with odometric sensors and signal transfer function data to provide means for different practical application scenarios. The sensor data fusion deals with asynchronous sensor data using inverse Laplace transform. We demonstrate a simulation software system that ensures smooth integration of the odometry-based and signal transfer - based localization into one approach.
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Dissertations / Theses on the topic "Odometri"

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Johansson, Sixten. "Navigering och styrning av ett autonomt markfordon." Thesis, Linköping University, Department of Electrical Engineering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-6006.

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I detta examensarbete har ett system för navigering och styrning av ett autonomt fordon implementerats. Syftet med detta arbete är att vidareutveckla fordonet som ska användas vid utvärdering av banplaneringsalgoritmer och studier av andra autonomifunktioner. Med hjälp av olika sensormodeller och sensorkonfigurationer går det även att utvärdera olika strategier för navigering. Arbetet har utförts utgående från en given plattform där fordonet endast använder sig av enkla ultraljudssensorer samt pulsgivare på hjulen för att mäta förflyttningar. Fordonet kan även autonomt navigera samt följa en enklare given bana i en känd omgivning. Systemet använder ett partikelfilter för att skatta fordonets tillstånd med hjälp av modeller för fordon och sensorer.

Arbetet är en fortsättning på projektet Collision Avoidance för autonomt fordon som genomfördes vid Linköpings universitet våren 2005.


In this thesis a system for navigation and control of an autonomous ground vehicle has been implemented. The purpose of this thesis is to further develop the vehicle that is to be used in studies and evaluations of path planning algorithms as well as studies of other autonomy functions. With different sensor configurations and sensor models it is also possible to evaluate different strategies for navigation. The work has been performed using a given platform which measures the vehicle’s movement using only simple ultrasonic sensors and pulse encoders. The vehicle is able to navigate autonomously and follow a simple path in a known environment. The state estimation is performed using a particle filter.

The work is a continuation of a previous project, Collision Avoidance för autonomt fordon, at Linköpings University in the spring of 2005.

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CHEN, HONGYI. "GPS-oscillation-robust Localization and Visionaided Odometry Estimation." Thesis, KTH, Maskinkonstruktion (Inst.), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-247299.

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GPS/IMU integrated systems are commonly used for vehicle navigation. The algorithm for this coupled system is normally based on Kalman filter. However, oscillated GPS measurements in the urban environment can lead to localization divergence easily. Moreover, heading estimation may be sensitive to magnetic interference if it relies on IMU with integrated magnetometer. This report tries to solve the localization problem on GPS oscillation and outage, based on adaptive extended Kalman filter(AEKF). In terms of the heading estimation, stereo visual odometry(VO) is fused to overcome the effect by magnetic disturbance. Vision-aided AEKF based algorithm is tested in the cases of both good GPS condition and GPS oscillation with magnetic interference. Under the situations considered, the algorithm is verified to outperform conventional extended Kalman filter(CEKF) and unscented Kalman filter(UKF) in position estimation by 53.74% and 40.09% respectively, and decrease the drifting of heading estimation.
GPS/IMU integrerade system används ofta för navigering av fordon. Algoritmen för detta kopplade system är normalt baserat på ett Kalmanfilter. Ett problem med systemet är att oscillerade GPS mätningar i stadsmiljöer enkelt kan leda till en lokaliseringsdivergens. Dessutom kan riktningsuppskattningen vara känslig för magnetiska störningar om den är beroende av en IMU med integrerad magnetometer. Rapporten försöker lösa lokaliseringsproblemet som skapas av GPS-oscillationer och avbrott med hjälp av ett adaptivt förlängt Kalmanfilter (AEKF). När det gäller riktningsuppskattningen används stereovisuell odometri (VO) för att försvaga effekten av magnetiska störningar genom sensorfusion. En Visionsstödd AEKF-baserad algoritm testas i fall med både goda GPS omständigheter och med oscillationer i GPS mätningar med magnetiska störningar. Under de fallen som är aktuella är algoritmen verifierad för att överträffa det konventionella utökade Kalmanfilteret (CEKF) och ”Unscented Kalman filter” (UKF) när det kommer till positionsuppskattning med 53,74% respektive 40,09% samt minska fel i riktningsuppskattningen.
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Pereira, Fabio Irigon. "High precision monocular visual odometry." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2018. http://hdl.handle.net/10183/183233.

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Extrair informação de profundidade a partir de imagens bidimensionais é um importante problema na área de visão computacional. Diversas aplicações se beneficiam desta classe de algoritmos tais como: robótica, a indústria de entretenimento, aplicações médicas para diagnóstico e confecção de próteses e até mesmo exploração interplanetária. Esta aplicação pode ser dividida em duas etapas interdependentes: a estimação da posição e orientação da câmera no momento em que a imagem foi gerada, e a estimativa da estrutura tridimensional da cena. Este trabalho foca em técnicas de visão computacional usadas para estimar a trajetória de um veículo equipado com uma câmera, problema conhecido como odometria visual. Para obter medidas objetivas de eficiência e precisão, e poder comparar os resultados obtidos com o estado da arte, uma base de dados de alta precisão, bastante utilizada pela comunidade científica foi utilizada. No curso deste trabalho novas técnicas para rastreamento de detalhes, estimativa de posição de câmera, cálculo de posição 3D de pontos e recuperação de escala são propostos. Os resultados alcançados superam os mais bem ranqueados trabalhos na base de dados escolhida até o momento da publicação desta tese.
Recovering three-dimensional information from bi-dimensional images is an important problem in computer vision that finds several applications in our society. Robotics, entertainment industry, medical diagnose and prosthesis, and even interplanetary exploration benefit from vision based 3D estimation. The problem can be divided in two interdependent operations: estimating the camera position and orientation when each image was produced, and estimating the 3D scene structure. This work focuses on computer vision techniques, used to estimate the trajectory of a vehicle equipped camera, a problem known as visual odometry. In order to provide an objective measure of estimation efficiency and to compare the achieved results to the state-of-the-art works in visual odometry a high precision popular dataset was selected and used. In the course of this work new techniques for image feature tracking, camera pose estimation, point 3D position calculation and scale recovery are proposed. The achieved results outperform the best ranked results in the popular chosen dataset.
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Porteš, Petr. "Návrh a realizace odometrických snímačů pro mobilní robot s Ackermannovým řízením." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2017. http://www.nusl.cz/ntk/nusl-318145.

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Aim of this thesis is to design and construct odometric sensors for a mobile robot with Ackermann steering Bender 2 and to design a mathematical model which would evaluate the the trajectory of the robot using measured data of these sensors. The first part summarizes theoretical knowledge, while the second, the practical part, describes the design of the front axle, the design and the operating software of the front encoders and the odometric models. The last part deals with the processing and evaluation of the measured data.
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Pärkkä, J. (Jarmo). "Reaaliaikainen visuaalinen odometria." Master's thesis, University of Oulu, 2013. http://urn.fi/URN:NBN:fi:oulu-201312021943.

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Visuaalisella odometrialla estimoidaan ajoneuvon, ihmisen tai robotin liikettä käyttäen syötteenä kuvaa yhdestä tai useammasta kamerasta. Sovelluskohteita on robotiikassa, autoteollisuudessa, asustemikroissa ja lisätyssä todellisuudessa. Se on hyvä lisä navigointijärjestelmiin, koska se toimii ympäristöissä missä GPS ei toimi. Visuaalinen odometria kehitettiin pyöräodometrian korvaajaksi, koska sen käyttö ei ole riippuvainen maastosta ja liikkumismuodosta. Tässä työssä tutkitaan ja kehitetään visuaalisen odometrian menetelmää reaaliaikaiseen sulautettuun järjestelmään. Työssä esitellään visuaalisen odometrian perusteet ja sen sisältämät osamenetelmät. Lisäksi esitellään yhtäaikainen paikallistaminen ja kartoitus (SLAM), jonka osana visuaalinen odometria voi esiintyä. Kehitettyä visuaalisen odometrian menetelmää on tarkoituksena käyttää Parrotin robottihelikopterille AR.Drone 2.0:lle tunnistamaan sen liikkeet. Tällöin robottihelikopteri saa tarpeeksi tietoa ympäristöstään lentääkseen itsenäisesti. Työssä toteutetaan algoritmi robotin tallentaman videomateriaalin tulkitsemiseen. Työssä toteutettu menetelmä on monokulaarinen SLAM, jossa käytetään yhden pisteen RANSAC-menetelmää yhdistettynä käänteisen syvyyden EKF:ään. Menetelmän piirteenirroitus ja vastinpisteiden etsintä korvataan reaaliaikaisella sulautetulle järjestelmälle sopivalla menetelmällä. Algoritmin toiminta testataan mittaamalla sen suoritusaika useilla kuvasekvensseillä ja analysoimalla sen piirtämää karttaa kameran liikkeestä. Lisäksi tarkastellaan sen antamien navigointitietojen todenmukaisuutta. Toteutetun järjestelmän toimintaa analysoidaan visuaalisesti ja sen toimintaa tarkastellaan suhteessa vertailumenetelmään. Työssä toteutettu visuaalisen odometrian menetelmä todetaan toimivaksi ratkaisuksi reaaliaikaiselle sulautetulle järjestelmälle tietyt rajoitukset huomioiden
Visual odometry is the process of estimating the motion of a vehicle, human or robot using the input of a single or multiple cameras. Application domains include robotics, wearable computing, augmented reality and automotive. It is a good supplement to the navigation systems because it operates in the environments where GPS does not. Visual odometry was developed as a substitute for wheel odometry, because its use is not dependent of the terrain. Visual odometry can be applied without restrictions to the way of movement (wheels, flying, walking). In this work visual odometry is examined and developed to be used in real-time embedded system. The basics of visual odometry are discussed. Furthermore, simultaneous localization and mapping (SLAM) is introduced. Visual odometry can appear as a part of SLAM. The purpose of this work is to develop visual odometry algorithm for Parrot’s robot helicopter AR.Drone 2.0, so it could fly independently in the future. The algorithm is based on Civera’s EKF-SLAM method, where feature extraction is replaced with an approach used earlier in global motion estimation. The operation of the algorithm is tested by measuring its performance time with different image sequences and by analyzing the movement of the camera from the map drawn by it. Furthermore, the reality of the navigation information is examined. The operation of the executed system is visually analyzed on the basis of the video and its operation is examined in relation to the comparison method. Developed visual odometry method is found to be a functional solution to the real-time embedded system under certain constraints
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Nishitani, André Toshio Nogueira. "Localização baseada em odometria visual." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-17082016-095838/.

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O problema da localização consiste em estimar a posição de um robô com relação a algum referencial externo e é parte essencial de sistemas de navegação de robôs e veículos autônomos. A localização baseada em odometria visual destaca-se em relação a odometria de encoders na obtenção da rotação e direção do movimento do robô. Esse tipo de abordagem é também uma escolha atrativa para sistemas de controle de veículos autônomos em ambientes urbanos, onde a informação visual é necessária para a extração de informações semânticas de placas, semáforos e outras sinalizações. Neste contexto este trabalho propõe o desenvolvimento de um sistema de odometria visual utilizando informação visual de uma câmera monocular baseado em reconstrução 3D para estimar o posicionamento do veículo. O problema da escala absoluta, inerente ao uso de câmeras monoculares, é resolvido utilizando um conhecimento prévio da relação métrica entre os pontos da imagem e pontos do mundo em um mesmo plano.
The localization problem consists of estimating the position of the robot with regards to some external reference and it is an essential part of robots and autonomous vehicles navigation systems. Localization based on visual odometry, compared to encoder based odometry, stands out at the estimation of rotation and direction of the movement. This kind of approach is an interesting choice for vehicle control systems in urban environment, where the visual information is mandatory for the extraction of semantic information contained in the street signs and marks. In this context this project propose the development of a visual odometry system based on structure from motion using visual information acquired from a monocular camera to estimate the vehicle pose. The absolute scale problem, inherent with the use of monocular cameras, is achieved using som previous known information regarding the metric relation between image points and points lying on a same world plane.
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Ligocki, Adam. "Metody současné sebelokalizace a mapování pro hloubkové kamery." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-316270.

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Tato diplomová práce se zabývá tvorbou fúze pozičních dat z existující realtimové im- plementace vizuálního SLAMu a kolové odometrie. Výsledkem spojení dat je potlačení nežádoucích chyb u každé ze zmíněných metod měření, díky čemuž je možné vytvořit přesnější 3D model zkoumaného prostředí. Práce nejprve uvádí teorií potřebnou pro zvládnutí problematiky 3D SLAMu. Dále popisuje vlastnosti použitého open source SLAM projektu a jeho jednotlivé softwarové úpravy. Následně popisuje principy spo- jení pozičních informací získaných vizuálními a odometrickými snímači, dále uvádí popis diferenciálního podvozku, který byl použit pro tvorbu kolové odometrie. Na závěr práce shrnuje výsledky dosažené datovou fúzí a srovnává je s původní přesností vizuálního SLAMu.
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Souza, Anderson Abner de Santana. "Mapeamento com Sonar Usando Grade de Ocupa??o baseado em Modelagem Probabil?stica." Universidade Federal do Rio Grande do Norte, 2008. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15203.

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In this work, we propose a probabilistic mapping method with the mapped environment represented through a modified occupancy grid. The main idea of the proposed method is to allow a mobile robot to construct in a systematic and incremental way the geometry of the underlying space, obtaining at the end a complete environment map. As a consequence, the robot can move in the environment in a safe way, based on a confidence value of data obtained from its perceptive system. The map is represented in a coherent way, according to its sensory data, being these noisy or not, that comes from exterior and proprioceptive sensors of the robot. Characteristic noise incorporated in the data from these sensors are treated by probabilistic modeling in such a way that their effects can be visible in the final result of the mapping process. The results of performed experiments indicate the viability of the methodology and its applicability in the area of autonomous mobile robotics, thus being an contribution to the field
Neste trabalho, propomos um m?todo de mapeamento probabil?stico com a representa??o do ambiente mapeado em uma grade de ocupa??o modificada. A id?ia principal do m?todo proposto ? deixar que um rob? m?vel construa de forma sistem?tica e incremental a geometria do seu entorno, obtendo ao final um mapa completo do ambiente. Como conseq??ncia, o rob? poder? locomover-se no seu ambiente de modo seguro, baseando-se em um ?ndice de confiabilidade dos dados colhidos do seu sistema perceptivo. O mapa ? representado de forma coerente com os dados sensoriais, sejam esses ruidosos ou n?o, oriundos dos sensores externoceptivos e proprioceptivos do rob?. Os ru?dos caracter?sticos incorporados nos dados de tais sensores s?o tratados por modelagem probabil?stica, de modo que seus efeitos possam ser vis?veis no resultado final do processo de mapeamento. Os resultados dos experimentos realizados, mostrados no presente trabalho, indicam a viabilidade desta metodologia e sua aplicabilidade na ?rea da rob?tica m?vel aut?noma, sendo assim uma contribui??o para a ?rea
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Silva, Bruno Marques Ferreira da. "Odometria visual baseada em t?cnicas de structure from motion." Universidade Federal do Rio Grande do Norte, 2011. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15364.

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Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior
Visual Odometry is the process that estimates camera position and orientation based solely on images and in features (projections of visual landmarks present in the scene) extraced from them. With the increasing advance of Computer Vision algorithms and computer processing power, the subarea known as Structure from Motion (SFM) started to supply mathematical tools composing localization systems for robotics and Augmented Reality applications, in contrast with its initial purpose of being used in inherently offline solutions aiming 3D reconstruction and image based modelling. In that way, this work proposes a pipeline to obtain relative position featuring a previously calibrated camera as positional sensor and based entirely on models and algorithms from SFM. Techniques usually applied in camera localization systems such as Kalman filters and particle filters are not used, making unnecessary additional information like probabilistic models for camera state transition. Experiments assessing both 3D reconstruction quality and camera position estimated by the system were performed, in which image sequences captured in reallistic scenarios were processed and compared to localization data gathered from a mobile robotic platform
Odometria Visual ? o processo pelo qual consegue-se obter a posi??o e orienta??o de uma c?mera, baseado somente em imagens e consequentemente, em caracter?sticas (proje??es de marcos visuais da cena) nelas contidas. Com o avan?o nos algoritmos e no poder de processamento dos computadores, a sub?rea de Vis?o Computacional denominada de Structure from Motion (SFM) passou a fornecer ferramentas que comp?em sistemas de localiza??o visando aplica??es como rob?tica e Realidade Aumentada, em contraste com o seu prop?sito inicial de ser usada em aplica??es predominantemente offline como reconstru??o 3D e modelagem baseada em imagens. Sendo assim, este trabalho prop?e um pipeline de obten??o de posi??o relativa que tem como caracter?sticas fazer uso de uma ?nica c?mera calibrada como sensor posicional e ser baseado interamente nos modelos e algoritmos de SFM. T?cnicas usualmente presentes em sistemas de localiza??o de c?mera como filtros de Kalman e filtros de part?culas n?o s?o empregadas, dispensando que informa??es adicionais como um modelo probabil?stico de transi??o de estados para a c?mera sejam necess?rias. Experimentos foram realizados com o prop?sito de avaliar tanto a reconstru??o 3D quanto a posi??o de c?mera retornada pelo sistema, atrav?s de sequ?ncias de imagens capturadas em ambientes reais de opera??o e compara??es com um ground truth fornecido pelos dados do od?metro de uma plataforma rob?tica
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Quist, Eric Blaine. "UAV Navigation and Radar Odometry." BYU ScholarsArchive, 2015. https://scholarsarchive.byu.edu/etd/4439.

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Prior to the wide deployment of robotic systems, they must be able to navigate autonomously. These systems cannot rely on good weather or daytime navigation and they must also be able to navigate in unknown environments. All of this must take place without human interaction. A majority of modern autonomous systems rely on GPS for position estimation. While GPS solutions are readily available, GPS is often lost and may even be jammed. To this end, a significant amount of research has focused on GPS-denied navigation. Many GPS-denied solutions rely on known environmental features for navigation. Others use vision sensors, which often perform poorly at high altitudes and are limited in poor weather. In contrast, radar systems accurately measure range at high and low altitudes. Additionally, these systems remain unaffected by inclimate weather. This dissertation develops the use of radar odometry for GPS-denied navigation. Using the range progression of unknown environmental features, the aircraft's motion is estimated. Results are presented for both simulated and real radar data. In Chapter 2 a greedy radar odometry algorithm is presented. It uses the Hough transform to identify the range progression of ground point-scatterers. A global nearest neighbor approach is implemented to perform data association. Assuming a piece-wise constant heading assumption, as the aircraft passes pairs of scatterers, the location of the scatterers are triangulated, and the motion of the aircraft is estimated. Real flight data is used to validate the approach. Simulated flight data explores the robustness of the approach when the heading assumption is violated. Chapter 3 explores a more robust radar odometry technique, where the relatively constant heading assumption is removed. This chapter uses the recursive-random sample consensus (R-RANSAC) Algorithm to identify, associate, and track the point scatterers. Using the measured ranges to the tracked scatterers, an extended Kalman filter (EKF) iteratively estimates the aircraft's position in addition to the relative locations of each reflector. Real flight data is used to validate the accuracy of this approach. Chapter 4 performs observability analysis of a range-only sensor. An observable, radar odometry approach is proposed. It improves the previous approaches by adding a more robust R-RANSAC above ground level (AGL) tracking algorithm to further improve the navigational accuracy. Real flight results are presented, comparing this approach to the techniques presented in previous chapters.
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Books on the topic "Odometri"

1

Illinois. Office of Secretary of State. Dept. of Police. Odometer fraud. [Springfield, Ill.]: Jesse White, Secretary of State, 2009.

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Sable, Robert. Odometer law. 2nd ed. Boston, MA: National Consumer Law Center, 1988.

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Ohio. Odometer Rollback and Disclosure Act. Columbus, Ohio: Attorney General's [Office], 1990.

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Illinois. Office of Secretary of State. Dept. of Police. Protecting yourself from odometer fraud. Springfield, Ill.]: State of Illinois Secretary of State Police, 1999.

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United States. Congress. Senate. Committee on Commerce, Science, and Transportation. Consumer protection from fraudulent motor vehicle odometer modifications: Report (to accompany S. 475). [Washington, D.C.?: U.S. G.P.O., 1985.

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Carter, Carolyn L. Automobile fraud: Odometer tampering, lemon laundering, and concealment of salvage or other adverse history. 3rd ed. Boston, MA: National Consumer Law Center, 2007.

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Sheldon, Jonathan A. Automobile fraud: Odometer tampering, lemon laundering, and concealment of salvage or other adverse history. 2nd ed. Boston, MA: National Consumer Law Center, 2003.

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Carter, Carolyn L. Automobile fraud: Odometer tampering, lemon laundering, and concealment of salvage or other adverse history. 3rd ed. Boston, MA: National Consumer Law Center, 2007.

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Odette, Williamson, Twomey Tara, Carter Carolyn L, and National Consumer Law Center, eds. Foreclosures: Defenses, workouts, and mortgage servicing. 2nd ed. Boston, MA: National Consumer Law Center, 2007.

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Rao, John, and Andrew G. Pizor. Foreclosures: Defenses, workouts, and mortgage servicing. 3rd ed. Boston, MA: National Consumer Law Center, 2010.

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Book chapters on the topic "Odometri"

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Harrison, Steven J., and M. T. Turvey. "Odometry." In Encyclopedia of Animal Cognition and Behavior, 1–5. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-47829-6_1474-1.

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Lamon, Pierre. "3D-Odometry." In Springer Tracts in Advanced Robotics, 21–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78287-2_3.

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Ben-Ari, Mordechai, and Francesco Mondada. "Robotic Motion and Odometry." In Elements of Robotics, 63–93. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62533-1_5.

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Lianos, Konstantinos-Nektarios, Johannes L. Schönberger, Marc Pollefeys, and Torsten Sattler. "VSO: Visual Semantic Odometry." In Computer Vision – ECCV 2018, 246–63. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01225-0_15.

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Röfer, Thomas. "Routenbeschreibung durch Odometrie-Scans." In Informatik aktuell, 122–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60043-2_15.

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Chien, Hsiang-Jen, Jr-Jiun Lin, Tang-Kai Yin, and Reinhard Klette. "Multi-objective Visual Odometry." In Image and Video Technology, 62–74. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75786-5_6.

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Santamaria-Navarro, A., J. Solà, and J. Andrade-Cetto. "Odometry Estimation for Aerial Manipulators." In Springer Tracts in Advanced Robotics, 219–28. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12945-3_15.

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Dudek, Gregory, and Michael Jenkin. "Inertial Sensing, GPS and Odometry." In Springer Handbook of Robotics, 737–52. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32552-1_29.

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Murphy, Liz, Timothy Morris, Ugo Fabrizi, Michael Warren, Michael Milford, Ben Upcroft, Michael Bosse, and Peter Corke. "Experimental Comparison of Odometry Approaches." In Experimental Robotics, 877–90. Heidelberg: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00065-7_58.

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Dudek, Gregory, and Michael Jenkin. "Inertial Sensors, GPS, and Odometry." In Springer Handbook of Robotics, 477–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-30301-5_21.

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Conference papers on the topic "Odometri"

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Zhu, Jianke. "Image Gradient-based Joint Direct Visual Odometry for Stereo Camera." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/636.

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Visual odometry is an important research problem for computer vision and robotics. In general, the feature-based visual odometry methods heavily rely on the accurate correspondences between local salient points, while the direct approaches could make full use of whole image and perform dense 3D reconstruction simultaneously. However, the direct visual odometry usually suffers from the drawback of getting stuck at local optimum especially with large displacement, which may lead to the inferior results. To tackle this critical problem, we propose a novel scheme for stereo odometry in this paper, which is able to improve the convergence with more accurate pose. The key of our approach is a dual Jacobian optimization that is fused into a multi-scale pyramid scheme. Moreover, we introduce a gradient-based feature representation, which enjoys the merit of being robust to illumination changes. Furthermore, a joint direct odometry approach is proposed to incorporate the information from the last frame and previous keyframes. We have conducted the experimental evaluation on the challenging KITTI odometry benchmark, whose promising results show that the proposed algorithm is very effective for stereo visual odometry.
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Anderson, J. Wesley, Joshua R. Fabian, and Garrett M. Clayton. "Adaptive RGB-D Visual Odometry for Mobile Robots: An Experimental Study." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9829.

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In this paper, experiments are presented in support of an adaptive color-depth (RGB-D) camera-based visual odometry algorithm. The goal of visual odometry is to estimate the egomotion of a robot using images from a camera attached to the robot. This type of measurement can be extremely useful when position sensor information, such as GPS, in unavailable and when error from other motion sensors (e.g., wheel encoders) is inaccurate (e.g., due to wheel slip). In the presented method, visual odometry algorithm parameters are adapted to ensure that odometry measurements are accurate while also considering computational cost. In this paper, live experiments are performed that show the feasibility of implementing the proposed algorithm on small wheeled mobile robots.
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Clayton, Garrett M., and Joshua R. Fabian. "Spatial Feature Matching for Visual Odometry: A Parametric Study." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3913.

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The goal of this paper is to perform a parametric study on a newly developed visual odometry algorithm for use with color-depth (RGB-D) camera pairs, such as the Microsoft Kinect. In this algorithm, features are detected in the color image and converted to 3D points using the depth image. These features are then described by their 3D location and matched across subsequent frames based on spatial proximity. The visual odometry is then calculated using a one-point inverse kinematic solution. The primary contribution of this work is the identification of critical operating parameters associated with the algorithm, the analysis of their effects on the visual odometry performance, and the verification of the analysis using experimentation.
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Wei, Peng, Guoliang Hua, Weibo Huang, Fanyang Meng, and Hong Liu. "Unsupervised Monocular Visual-inertial Odometry Network." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/325.

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Recently, unsupervised methods for monocular visual odometry (VO), with no need for quantities of expensive labeled ground truth, have attracted much attention. However, these methods are inadequate for long-term odometry task, due to the inherent limitation of only using monocular visual data and the inability to handle the error accumulation problem. By utilizing supplemental low-cost inertial measurements, and exploiting the multi-view geometric constraint and sequential constraint, an unsupervised visual-inertial odometry framework (UnVIO) is proposed in this paper. Our method is able to predict the per-frame depth map, as well as extracting and self-adaptively fusing visual-inertial motion features from image-IMU stream to achieve long-term odometry task. A novel sliding window optimization strategy, which consists of an intra-window and an inter-window optimization, is introduced for overcoming the error accumulation and scale ambiguity problem. The intra-window optimization restrains the geometric inferences within the window through checking the photometric consistency. And the inter-window optimization checks the 3D geometric consistency and trajectory consistency among predictions of separate windows. Extensive experiments have been conducted on KITTI and Malaga datasets to demonstrate the superiority of UnVIO over other state-of-the-art VO / VIO methods. The codes are open-source.
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Kutzer, Michael D., John S. Donnal, Gregory L. Sinsley, and Ryan S. McDowell. "Toward Detecting Cyber-Physical Attacks in Additive Manufacturing Using Multi-View Visual Odometry." In ASME 2020 15th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/msec2020-8299.

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Abstract This paper presents a method using multi-view visual odometry as an independent tool to reconstruct deposition trajectories in additive manufacturing processes. A physical testbed is presented including camera and encoder retrofits to a Lulzbot TAZ 6. The system, including added sensors, is interfaced using the Wattsworth decentralized IoT framework for data acquisition, preliminary processing, and storage. The proposed visual odometry method is presented, and preliminary testbed results show reliable encoder feedback and camera calibration for use as ground truth in future validation.
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Kleinschmidt, Sebastian P., and Bernardo Wagner. "Visual Multimodal Odometry: Robust Visual Odometry in Harsh Environments." In 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). IEEE, 2018. http://dx.doi.org/10.1109/ssrr.2018.8468653.

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Трубаков, Евгений, Evgeniy Trubakov, Ольга Трубакова, and Olga Trubakova. "Comparative Analysis of Monocular Visual Odometry Methods for Indoor Navigation." In 29th International Conference on Computer Graphics, Image Processing and Computer Vision, Visualization Systems and the Virtual Environment GraphiCon'2019. Bryansk State Technical University, 2019. http://dx.doi.org/10.30987/graphicon-2019-2-304-307.

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The monocular visual odometry algorithm involves several basic steps and for each of them there is a number of methods. The purpose of this work is to conduct practical research of methods for key point detection and the optical flow calculation in the problem of determining the unmanned ground vehicle proper motion. For detection method research conduction the image panel containing various distortions typical for follow robot shot was made. It has been educed that among the accounted methods FAST finds the largest number of points within the minimum elapsed time. At the same time image distortions strongly affect the results of the method, which is negative for the positioning of the robot. Therefore the Shi-Tomasi method was chosen for key points detection within a short period of time, because its results are less dependent on distortion. For research undertake a number of video clips by means of the follow robot shot was made in a confined space at a later scale of the odometry algorithm. From experimental observations the conclusions concerning the application of Lucas-Kanade optical flow method tracking the identified points on the video sequence have been made. Based on the error in the results obtained it was implication that monocular odometry cannot be the main method of an unmanned vehicle positioning in confined spaces, but in combination with probe data given by assistive sensors it is quite possible to use it for determining the robotic system position.
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Lin, Minjie, Qixin Cao, and Haoruo Zhang. "PVO:Panoramic Visual Odometry." In 2018 3rd International Conference on Advanced Robotics and Mechatronics (ICARM). IEEE, 2018. http://dx.doi.org/10.1109/icarm.2018.8610700.

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Center, Julian L., Kevin H. Knuth, Ali Mohammad-Djafari, Jean-François Bercher, and Pierre Bessiére. "Bayesian Visual Odometry." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: Proceedings of the 30th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2011. http://dx.doi.org/10.1063/1.3573659.

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Carey, Kevin, Benjamin Abruzzo, David P. Harvie, and Christopher Korpela. "Performance Comparison of Inertial Measurement Units Fused With Odometry in Extended Kalman Filter for Dead-Reckoning Navigation." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-98184.

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Abstract This paper aims to aid robot and autonomous vehicle designers by providing a comparison between four different inertial measurement units (IMUs) which could be used to aid in vehicle navigation in a GPS-denied or inertial-only scenario. A differential-drive ground vehicle was designed to carry the multiple different IMUs, mounted coaxially, to enable direct comparison of performance in a planar environment. The experiments focused on the growth of pose error of the ground vehicle originating from the odometry senors and the IMUs. An extended Kalman Filter was developed to fuse the odometry and inertial measurements for this comparison. The four specific IMUs evaluated were: CNS 5000, Xsens 300, Microstrain GX5-35, and Phidgets 1044 and the ground truth for experiments was provided by an Optitrack motion capture system (MCS). Finally, metrics for choosing IMUs, merging cost and performance considerations, are proposed and discussed. While the CNS 5000 has the best objective error specifications, based on these metrics the Xsens 300 exhibits the best absolute performance while the Phidgets 1044 provides the best performance-per-dollar.
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Reports on the topic "Odometri"

1

Pirozzo, David M., Philip A. Frederick, Shawn Hunt, Bernard Theisen, and Mike Del Rose. Spectrally Queued Feature Selection for Robotic Visual Odometery. Fort Belvoir, VA: Defense Technical Information Center, November 2010. http://dx.doi.org/10.21236/ada535663.

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