Thematische Bibliographien / Odometry estimation

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Auswahl der wissenschaftlichen Literatur zum Thema „Odometry estimation“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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Zeitschriftenartikel zum Thema "Odometry estimation"

Wang, Chenggong, Gen Li, Ruiqi Wang, and Lin Li. "Wheeled Robot Visual Odometer Based on Two-dimensional Iterative Closest Point Algorithm." Journal of Physics: Conference Series 2504, no. 1 (2023): 012002. http://dx.doi.org/10.1088/1742-6596/2504/1/012002.

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Abstract According to the two-dimensional motion characteristics of planar motion wheeled robot, the visual odometer was dimensionally reduced in this study. In the feature point matching part of visual odometer, the contour constraint was used to filter out the mismatched feature point pairs (abbreviated as FPP). This method could also filter out the matched FPP, and the feature of FPP was correct color image matches, however, their depth image error was large. This offered higher quality matched FPP for the subsequent interframe motion estimation. Dimension reduction was performed in the int

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Nurmaini, Siti, and Sahat Pangidoan. "Localization of Leader-Follower Robot Using Extended Kalman Filter." Computer Engineering and Applications Journal 7, no. 2 (2018): 95–108. http://dx.doi.org/10.18495/comengapp.v7i2.253.

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Non-holonomic leader-follower robot must be capable to find its own position in order to be able to navigating autonomously in the environment this problem is known as localization. A common way to estimate the robot pose by using odometer. However, odometry measurement may cause inaccurate result due to the wheel slippage or other small noise sources. In this research, the Extended Kalman Filter (EKF) is proposed to minimize the error or the inaccuracy caused by the odometry measurement. The EKF algorithm works by fusing odometry and landmark information to produce a better estimation. A bett

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Li, Q., C. Wang, S. Chen, et al. "DEEP LIDAR ODOMETRY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1681–86. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1681-2019.

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<p><strong>Abstract.</strong> Most existing lidar odometry estimation strategies are formulated under a standard framework that includes feature selection, and pose estimation through feature matching. In this work, we present a novel pipeline called LO-Net for lidar odometry estimation from 3D lidar scanning data using deep convolutional networks. The network is trained in an end-to-end manner, it infers 6-DoF poses from the encoded sequential lidar data. Based on the new designed mask-weighted geometric constraint loss, the network automatically learns effective feature rep

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Eising, Ciarán, Leroy‐Francisco Pereira, Jonathan Horgan, Anbuchezhiyan Selvaraju, John McDonald, and Paul Moran. "2.5D vehicle odometry estimation." IET Intelligent Transport Systems 16, no. 3 (2021): 292–308. http://dx.doi.org/10.1049/itr2.12143.

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Wan, Yingcai, Qiankun Zhao, Cheng Guo, Chenlong Xu, and Lijing Fang. "Multi-Sensor Fusion Self-Supervised Deep Odometry and Depth Estimation." Remote Sensing 14, no. 5 (2022): 1228. http://dx.doi.org/10.3390/rs14051228.

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This paper presents a new deep visual-inertial odometry and depth estimation framework for improving the accuracy of depth estimation and ego-motion from image sequences and inertial measurement unit (IMU) raw data. The proposed framework predicts ego-motion and depth with absolute scale in a self-supervised manner. We first capture dense features and solve the pose by deep visual odometry (DVO), and then combine the pose estimation pipeline with deep inertial odometry (DIO) by the extended Kalman filter (EKF) method to produce the sparse depth and pose with absolute scale. We then join deep v

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Zhao, Zixu, Yucheng Zhang, Jinglin Shi, Long Long, and Zaiwang Lu. "Robust Lidar-Inertial Odometry with Ground Condition Perception and Optimization Algorithm for UGV." Sensors 22, no. 19 (2022): 7424. http://dx.doi.org/10.3390/s22197424.

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Unmanned ground vehicles (UGVs) are making more and more progress in many application scenarios in recent years, such as exploring unknown wild terrain, working in precision agriculture and serving in emergency rescue. Due to the complex ground conditions and changeable surroundings of these unstructured environments, it is challenging for these UGVs to obtain robust and accurate state estimations by using sensor fusion odometry without prior perception and optimization for specific scenarios. In this paper, based on an error-state Kalman filter (ESKF) fusion model, we propose a robust lidar-i

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Chen, Baifan, Haowu Zhao, Ruyi Zhu, and Yemin Hu. "Marked-LIEO: Visual Marker-Aided LiDAR/IMU/Encoder Integrated Odometry." Sensors 22, no. 13 (2022): 4749. http://dx.doi.org/10.3390/s22134749.

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In this paper, we propose a visual marker-aided LiDAR/IMU/encoder integrated odometry, Marked-LIEO, to achieve pose estimation of mobile robots in an indoor long corridor environment. In the first stage, we design the pre-integration model of encoder and IMU respectively to realize the pose estimation combined with the pose estimation from the second stage providing prediction for the LiDAR odometry. In the second stage, we design low-frequency visual marker odometry, which is optimized jointly with LiDAR odometry to obtain the final pose estimation. In view of the wheel slipping and LiDAR deg

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Zhao, Zixu, Yucheng Zhang, Long Long, Zaiwang Lu, and Jinglin Shi. "Efficient and adaptive lidar–visual–inertial odometry for agricultural unmanned ground vehicle." International Journal of Advanced Robotic Systems 19, no. 2 (2022): 172988062210949. http://dx.doi.org/10.1177/17298806221094925.

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The accuracy of agricultural unmanned ground vehicles’ localization directly affects the accuracy of their navigation. However, due to the changeable environment and fewer features in the agricultural scene, it is challenging for these unmanned ground vehicles to localize precisely in global positioning system-denied areas with a single sensor. In this article, we present an efficient and adaptive sensor-fusion odometry framework based on simultaneous localization and mapping to handle the localization problems of agricultural unmanned ground vehicles without the assistance of a global positio

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Qiu, Haiyang, Xu Zhang, Hui Wang, et al. "A Robust and Integrated Visual Odometry Framework Exploiting the Optical Flow and Feature Point Method." Sensors 23, no. 20 (2023): 8655. http://dx.doi.org/10.3390/s23208655.

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In this paper, we propose a robust and integrated visual odometry framework exploiting the optical flow and feature point method that achieves faster pose estimate and considerable accuracy and robustness during the odometry process. Our method utilizes optical flow tracking to accelerate the feature point matching process. In the odometry, two visual odometry methods are used: global feature point method and local feature point method. When there is good optical flow tracking and enough key points optical flow tracking matching is successful, the local feature point method utilizes prior info

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Martínez-García, Edgar Alonso, Joaquín Rivero-Juárez, Luz Abril Torres-Méndez, and Jorge Enrique Rodas-Osollo. "Divergent trinocular vision observers design for extended Kalman filter robot state estimation." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 233, no. 5 (2018): 524–47. http://dx.doi.org/10.1177/0959651818800908.

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Here, we report the design of two deterministic observers that exploit the capabilities of a home-made divergent trinocular visual sensor to sense depth data. The three-dimensional key points that the observers can measure are triangulated for visual odometry and estimated by an extended Kalman filter. This work deals with a four-wheel-drive mobile robot with four passive suspensions. The direct and inverse kinematic solutions are deduced and used for the updating and prediction models of the extended Kalman filter as feedback for the robot’s position controller. The state-estimation visual od

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Dissertationen zum Thema "Odometry estimation"

Masson, Clément. "Direction estimation using visual odometry." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-169377.

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This Master thesis tackles the problem of measuring objects’ directions from a motionlessobservation point. A new method based on a single rotating camera requiring the knowledge ofonly two (or more) landmarks’ direction is proposed. In a first phase, multi-view geometry isused to estimate camera rotations and key elements’ direction from a set of overlapping images.Then in a second phase, the direction of any object can be estimated by resectioning the cameraassociated to a picture showing this object. A detailed description of the algorithmic chain isgiven, along with test results on both sy

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Holmqvist, Niclas. "HANDHELD LIDAR ODOMETRY ESTIMATION AND MAPPING SYSTEM." Thesis, Mälardalens högskola, Inbyggda system, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-41137.

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Ego-motion sensors are commonly used for pose estimation in Simultaneous Localization And Mapping (SLAM) algorithms. Inertial Measurement Units (IMUs) are popular sensors but suffer from integration drift over longer time scales. To remedy the drift they are often used in combination with additional sensors, such as a LiDAR. Pose estimation is used when scans, produced by these additional sensors, are being matched. The matching of scans can be computationally heavy as one scan can contain millions of data points. Methods exist to simplify the problem of finding the relative pose between senso

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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 m

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Rao, Anantha N. "Learning-based Visual Odometry - A Transformer Approach." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627658636420617.

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Awang, Salleh Dayang Nur Salmi Dharmiza. "Study of vehicle localization optimization with visual odometry trajectory tracking." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS601.

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Au sein des systèmes avancés d’aide à la conduite (Advanced Driver Assistance Systems - ADAS) pour les systèmes de transport intelligents (Intelligent Transport Systems - ITS), les systèmes de positionnement, ou de localisation, du véhicule jouent un rôle primordial. Le système GPS (Global Positioning System) largement employé ne peut donner seul un résultat précis à cause de facteurs extérieurs comme un environnement contraint ou l’affaiblissement des signaux. Ces erreurs peuvent être en partie corrigées en fusionnant les données GPS avec des informations supplémentaires provenant d'autres ca

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Warren, Michael David. "Long-range stereo visual odometry for unmanned aerial vehicles." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/80107/1/Michael_Warren_Thesis.pdf.

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This thesis explored the utility of long-range stereo visual odometry for application on Unmanned Aerial Vehicles. Novel parameterisations and initialisation routines were developed for the long-range case of stereo visual odometry and new optimisation techniques were implemented to improve the robustness of visual odometry in this difficult scenario. In doing so, the applications of stereo visual odometry were expanded and shown to perform adequately in situations that were previously unworkable.

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Nazifi, Nahid. "Transformer-Based Visual Odometry and DepthEstimation for Wireless Capsule Endoscopy." Electronic Thesis or Diss., Bourges, INSA Centre Val de Loire, 2025. http://www.theses.fr/2025ISAB0002.

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L'estimation précise de la pose et de la profondeur pour l'endoscopie par capsule (Wireless Capsule Endoscopy, WCE) demeure un défi majeur en raison de la nature non structurée et pauvre en textures du tractus gastro-intestinal (GI). Cette thèse explore l'utilisation d'architectures basées sur les transformateurs pour l'estimation auto-supervisée de la profondeur et de la pose monoculaires en WCE. Contrairement aux méthodes traditionnelles d'odométrie visuelle, qui reposent sur des techniques basées sur des points d'intérêt, les approches proposées exploitent le Pyramid Vision Transformer (PVT

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Ay, Emre. "Ego-Motion Estimation of Drones." Thesis, KTH, Robotik, perception och lärande, RPL, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210772.

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To remove the dependency on external structure for drone positioning in GPS-denied environments, it is desirable to estimate the ego-motion of drones on-board. Visual positioning systems have been studied for quite some time and the literature on the area is diligent. The aim of this project is to investigate the currently available methods and implement a visual odometry system for drones which is capable of giving continuous estimates with a lightweight solution. In that manner, the state of the art systems are investigated and a visual odometry system is implemented based on the design deci

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Lee, Hong Yun. "Deep Learning for Visual-Inertial Odometry: Estimation of Monocular Camera Ego-Motion and its Uncertainty." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu156331321922759.

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Ringdahl, Viktor. "Stereo Camera Pose Estimation to Enable Loop Detection." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-154392.

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Visual Simultaneous Localization And Mapping (SLAM) allows for three dimensionalreconstruction from a camera’s output and simultaneous positioning of the camera withinthe reconstruction. With use cases ranging from autonomous vehicles to augmentedreality, the SLAM field has garnered interest both commercially and academically. A SLAM system performs odometry as it estimates the camera’s movement throughthe scene. The incremental estimation of odometry is not error free and exhibits driftover time with map inconsistencies as a result. Detecting the return to a previously seenplace, a loop, mean

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Buchteile zum Thema "Odometry estimation"

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

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Yue, Hao, and Yun Gu. "TCL: Triplet Consistent Learning for Odometry Estimation of Monocular Endoscope." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43996-4_14.

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Poddar, Shashi, Rahul Kottath, and Vinod Karar. "Motion Estimation Made Easy: Evolution and Trends in Visual Odometry." In Recent Advances in Computer Vision. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03000-1_13.

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Clement, Lee, Valentin Peretroukhin, and Jonathan Kelly. "Improving the Accuracy of Stereo Visual Odometry Using Visual Illumination Estimation." In Springer Proceedings in Advanced Robotics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50115-4_36.

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Guerrero, Pablo, and Javier Ruiz-del-Solar. "Improving Robot Self-localization Using Landmarks’ Poses Tracking and Odometry Error Estimation." In RoboCup 2007: Robot Soccer World Cup XI. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-68847-1_13.

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Punčochář, Ivo, and Jan Taufer. "Estimation of Train Speed and Travelled Distance Using Odometry and Partial IMU." In Lecture Notes in Control and Information Sciences - Proceedings. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85318-1_68.

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Nguyen, Huu Hung, Quang Thi Nguyen, Cong Manh Tran, and Dong-Seong Kim. "Adaptive Essential Matrix Based Stereo Visual Odometry with Joint Forward-Backward Translation Estimation." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63083-6_10.

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Galarza, Juan, Esteban Pérez, Esteban Serrano, Andrés Tapia, and Wilbert G. Aguilar. "Pose Estimation Based on Monocular Visual Odometry and Lane Detection for Intelligent Vehicles." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95282-6_40.

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Wang, Wufan, and Lei Zhang. "Semi-direct Sparse Odometry with Robust and Accurate Pose Estimation for Dynamic Scenes." In Computer-Aided Design and Computer Graphics. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9666-7_9.

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Zhang, Xue-bo, Cong-yuan Wang, Yong-chun Fang, and Ke-xin Xing. "An Extended Kalman Filter-Based Robot Pose Estimation Approach with Vision and Odometry." In Wearable Sensors and Robots. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2404-7_41.

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Konferenzberichte zum Thema "Odometry estimation"

Michaelis, Martin, Philipp Berthold, Thorsten Luettel, and Mirko Maehlisch. "Multimodal Odometry Estimation With Automated Sensor Selection." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706356.

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Saleem, Hajira, Reza Malekian, and Hussan Munir. "Enhancing Visual Odometry Estimation Performance Using Image Enhancement Models." In 21st International Conference on Informatics in Control, Automation and Robotics. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012932600003822.

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Lv, Yuezhang, Yunzhou Zhang, Xiaoyu Zhao, Wu Li, Jian Ning, and Yang Jin. "CTA-LO: Accurate and Robust LiDAR Odometry Using Continuous-Time Adaptive Estimation." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611453.

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Huang, Jui-Te, Ruoyang Xu, Akshay Hinduja, and Michael Kaess. "Multi-Radar Inertial Odometry for 3D State Estimation using mmWave Imaging Radar." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611194.

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Brühl, Tim, Tim Dieter Eberhardt, Robin Schwager, Lukas Ewecker, Tin Stribor Sohn, and Sören Hohmann. "Odometry Estimation by Fusing Multiple Radar Sensors and an Inertial Measurement Unit." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610446.

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Wei, Fengchen, and Weiji Wang. "OFVO: A Visual Odometry Designed for Motion Trajectory Estimation of Autonomous Vehicles." In 2024 4th International Conference on Robotics, Automation and Artificial Intelligence (RAAI). IEEE, 2024. https://doi.org/10.1109/raai64504.2024.10949531.

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Diaz, Aldo, and Paulo Kurka. "Absolute Scale Estimation Approach for Monocular Visual Odometry." In LatinX in AI at Computer Vision and Pattern Recognition Conference 2021. Journal of LatinX in AI Research, 2021. http://dx.doi.org/10.52591/lxai2021062515.

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Monocular visual odometry is an effective motion estimation technique that requires to solve for the challenging problem of absolute (metric) scale estimation. Current approaches use information such as the camera height or size of known objects to estimate the scene scale. In this paper, we propose a novel prediction-correction method to estimate the absolute scale of motion using camera height and flat ground assumption. Prediction is provided by a robust relative scale estimation strategy that exploits redundancy in depth information. Correction implements ground patch correlation using sub

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Diaz, Aldo, and Paulo Kurka. "Relative scale estimation approach for monocular visual odometry." In LatinX in AI at Computer Vision and Pattern Recognition Conference 2021. Journal of LatinX in AI Research, 2021. http://dx.doi.org/10.52591/lxai2021062516.

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Determining the scale of relative motion is key to achieve consistency in monocular motion estimation when trajectories are recovered up to a scale factor. In this paper, we introduce a novel method to estimate the relative scale in monocular visual odometry using a calibrated camera. Our algorithm exploits redundancy in point depth information to achieve robust relative scale estimates. The performance of the method is evaluated in the KITTI public dataset for autonomous vehicles using the standard KITTI benchmark metrics. The results demonstrate the effectiveness of a robust relative scale e

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Pereira, Fabio Irigon, Gustavo Ilha, Joel Luft, Marcelo Negreiros, and Altamiro Susin. "Monocular Visual Odometry with Cyclic Estimation." In 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2017. http://dx.doi.org/10.1109/sibgrapi.2017.7.

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Ross, Derek, Matteo De Petrillo, Jared Strader, and Jason N. Gross. "Uncertainty Estimation for Stereo Visual Odometry." In 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021). Institute of Navigation, 2021. http://dx.doi.org/10.33012/2021.18063.

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