Academic literature on the topic 'Indoor magnetometer calibration. system'
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Journal articles on the topic "Indoor magnetometer calibration. system"
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Chow, J. C. K. "STATISTICAL SENSOR FUSION OF A 9-DOF MEMS IMU FOR INDOOR NAVIGATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 333–38. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-333-2017.
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Sensor fusion of a MEMS IMU with a magnetometer is a popular system design, because such 9-DoF (degrees of freedom) systems are capable of achieving drift-free 3D orientation tracking. However, these systems are often vulnerable to ambient magnetic distortions and lack useful position information; in the absence of external position aiding (e.g. satellite/ultra-wideband positioning systems) the dead-reckoned position accuracy from a 9-DoF MEMS IMU deteriorates rapidly due to unmodelled errors. Positioning information is valuable in many satellite-denied geomatics applications (e.g. indoor navigation, location-based services, etc.). This paper proposes an improved 9-DoF IMU indoor pose tracking method using batch optimization. By adopting a robust in-situ user self-calibration approach to model the systematic errors of the accelerometer, gyroscope, and magnetometer simultaneously in a tightly-coupled post-processed least-squares framework, the accuracy of the estimated trajectory from a 9-DoF MEMS IMU can be improved. Through a combination of relative magnetic measurement updates and a robust weight function, the method is able to tolerate a high level of magnetic distortions. The proposed auto-calibration method was tested in-use under various heterogeneous magnetic field conditions to mimic a person walking with the sensor in their pocket, a person checking their phone, and a person walking with a smartwatch. In these experiments, the presented algorithm improved the in-situ dead-reckoning orientation accuracy by 79.8–89.5 % and the dead-reckoned positioning accuracy by 72.9–92.8 %, thus reducing the relative positioning error from metre-level to decimetre-level after ten seconds of integration, without making assumptions about the user’s dynamics.
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Ma, Ming, Qian Song, Yang Gu, and Zhimin Zhou. "Use of Magnetic Field for Mitigating Gyroscope Errors for Indoor Pedestrian Positioning." Sensors 18, no. 8 (August 7, 2018): 2592. http://dx.doi.org/10.3390/s18082592.
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In the field of indoor pedestrian positioning, the improved Quasi-Static magnetic Field (iQSF) method has been proposed to estimate gyroscope biases in magnetically perturbed environments. However, this method is only effective when a person walks along straight-line paths. For other curved or more complex path patterns, the iQSF method would fail to detect the quasi-static magnetic field. To address this issue, a novel approach is developed for quasi-static magnetic field detection in foot-mounted Inertial Navigation System. The proposed method detects the quasi-static magnetic field using the rate of change in differences between the magnetically derived heading and the heading derived from gyroscope. In addition, to eliminate the distortions caused by system platforms and shoes, a magnetometer calibration method is developed and the calibration is transformed from three-dimensional to two-dimensional coordinate according to the motion model of a pedestrian. The experimental results demonstrate that the proposed method can provide superior performance in suppressing the heading errors with the comparison to iQSF method.
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Li, You, Shady Zahran, Yuan Zhuang, Zhouzheng Gao, Yiran Luo, Zhe He, Ling Pei, Ruizhi Chen, and Naser El-Sheimy. "IMU/Magnetometer/Barometer/Mass-Flow Sensor Integrated Indoor Quadrotor UAV Localization with Robust Velocity Updates." Remote Sensing 11, no. 7 (April 8, 2019): 838. http://dx.doi.org/10.3390/rs11070838.
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Velocity updates have been proven to be important for constraining motion-sensor-based dead-reckoning (DR) solutions in indoor unmanned aerial vehicle (UAV) applications. The forward velocity from a mass flow sensor and the lateral and vertical non-holonomic constraints (NHC) can be utilized for three-dimensional (3D) velocity updates. However, it is observed that (a) the quadrotor UAV may have a vertical velocity trend when it is controlled to move horizontally; (b) the quadrotor may have a pitch angle when moving horizontally; and (c) the mass flow sensor may suffer from sensor errors, especially the scale factor error. Such phenomenons degrade the performance of velocity updates. Thus, this paper presents a multi-sensor integrated localization system that has more effective sensor interactions. Specifically, (a) the barometer data are utilized to detect height changes and thus determine the weight of vertical velocity update; (b) the pitch angle from the inertial measurement unit (IMU) and magnetometer data fusion is used to set the weight of forward velocity update; and (c) an extra mass flow sensor calibration module is introduced. Indoor flight tests have indicated the effectiveness of the proposed sensor interaction strategies in enhancing indoor quadrotor DR solutions, which can also be used for detecting outliers in external localization technologies such as ultrasonics.
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Yang, Wei, Chundi Xiu, Jiarui Ye, Zhixing Lin, Haisong Wei, Dayu Yan, and Dongkai Yang. "LSS-RM: Using Multi-Mounted Devices to Construct a Lightweight Site-Survey Radio Map for WiFi Positioning." Micromachines 9, no. 9 (September 12, 2018): 458. http://dx.doi.org/10.3390/mi9090458.
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A WiFi-received signal strength index (RSSI) fingerprinting-based indoor positioning system (WiFi-RSSI IPS) is widely studied due to advantages of low cost and high accuracy, especially in a complex indoor environment where performance of the ranging method is limited. The key drawback that limits the large-scale deployment of WiFi-RSSI IPS is time-consuming offline site surveys. To solve this problem, we developed a method using multi-mounted devices to construct a lightweight site-survey radio map (LSS-RM) for WiFi positioning. A smartphone was mounted on the foot (Phone-F) and another on the waist (Phone-W) to scan WiFi-RSSI and simultaneously sample microelectromechanical system inertial measurement-unit (MEMS-IMU) readings, including triaxial accelerometer, gyroscope, and magnetometer measurements. The offline site-survey phase in LSS-RM is a client–server model of a data collection and preprocessing process, and a post calibration process. Reference-point (RP) coordinates were estimated using the pedestrian dead-reckoning algorithm. The heading was calculated with a corner detected by Phone-W and the preassigned site-survey trajectory. Step number and stride length were estimated using Phone-F based on the stance-phase detection algorithm. Finally, the WiFi-RSSI radio map was constructed with the RP coordinates and timestamps of each stance phase. Experimental results show that our LSS-RM method can reduce the time consumption of constructing a WiFi-RSSI radio map from 54 min to 7.6 min compared with the manual site-survey method. The average positioning error was below 2.5 m with three rounds along the preassigned site-survey trajectory. LSS-RM aims to reduce offline site-survey time consumption, which would cut down on manpower. It can be used in the large-scale implementation of WiFi-RSSI IPS, such as shopping malls, hospitals, and parking lots.
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Muraccini, Marco, Anna Mangia, Maurizio Lannocca, and Angelo Cappello. "Magnetometer Calibration and Field Mapping through Thin Plate Splines." Sensors 19, no. 2 (January 11, 2019): 280. http://dx.doi.org/10.3390/s19020280.
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While the undisturbed Earth’s magnetic field represents a fundamental information source for orientation purposes, magnetic distortions have been mostly considered as a source of error. However, when distortions are temporally stable and spatially distinctive, they could provide a unique magnetic landscape that can be used in different applications, from indoor localization to sensor fusion algorithms for attitude estimation. The main purpose of this work, therefore, is to present a method to characterize the 3D magnetic vector in every point of the measurement volume. The possibility of describing the 3D magnetic field map through Thin Plate Splines (TPS) interpolation is investigated and demonstrated. An algorithm for the simultaneous estimation of the parameters related to magnetometer calibration and those describing the magnetic map, is proposed and tested on both simulated and real data. Results demonstrate that an accurate description of the local magnetic field using TPS interpolation is possible. The proposed procedure leads to errors in the estimation of the local magnetic direction with a standard deviation lower than 1 degree. Magnetometer calibration and magnetic field mapping could be integrated into different algorithms, for example to improve attitude estimation in highly distorted environments or as an aid to indoor localization.
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Martins-Filho, Luiz S., and Jader De Amorim. "Experimental Magnetometer Calibration for Nanosatellites’ Navigation System." Journal of Aerospace Technology and Management 8, no. 1 (March 7, 2016): 103–12. http://dx.doi.org/10.5028/jatm.v8i1.586.
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Koprivica, Branko, Marko Sucurovic, and Alenka Milovanovic. "Calibration of ac induction magnetometer." Facta universitatis - series: Electronics and Energetics 31, no. 4 (2018): 613–26. http://dx.doi.org/10.2298/fuee1804613k.
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The aim of this paper is to describe a procedure and experimental setup for calibration of AC induction magnetometer. The paper presents an overview of the previous research and results of measurement of magnetic flux density inside large diameter multilayer solenoid. This solenoid is magnetising coil of the magnetometer. The paper also describes a system of five smaller coils of the magnetometer which are placed inside the large solenoid. Three small coils are pickup coils, accompanied with two compensation coils, of which one is an empty coil for magnetic field measurement. The experimental results of calibration of this coil system have been presented. A proper discussion of all the results presented has been also given in the paper.
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Nevzorov, A. A., A. A. Orlov, V. K. Ignatjev, and A. A. Bardin. "Calibration algorithm of Hall magnetometer in visible coordinate system." Measurement 134 (February 2019): 939–46. http://dx.doi.org/10.1016/j.measurement.2018.11.065.
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Feng, Pan, Danyang Qin, Min Zhao, Ruolin Guo, and Teklu Berhane. "Unsupervised Indoor Positioning System Based on Environmental Signatures." Entropy 21, no. 3 (March 26, 2019): 327. http://dx.doi.org/10.3390/e21030327.
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Mobile sensors are widely used in indoor positioning in recent years, but most methods require cumbersome calibration for precise positioning results, thus the paper proposes a new unsupervised indoor positioning (UIP) without cumbersome calibration. UIP takes advantage of environment features in indoor environments, as some indoor locations have their signatures. UIP considers these signatures as the landmarks, and combines dead reckoning with them in a simultaneous localization and mapping (SLAM) frame to reduce positioning errors and convergence time. The test results prove that the system can achieve accurate indoor positioning, which highlights its prospect as an unconventional method of indoor positioning.
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Long, Dafeng, Xiaoming Zhang, Xiaohui Wei, Zhongliang Luo, and Jianzhong Cao. "A Fast Calibration and Compensation Method for Magnetometers in Strap-Down Spinning Projectiles." Sensors 18, no. 12 (November 27, 2018): 4157. http://dx.doi.org/10.3390/s18124157.
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Attitude measurement is an essential technology in projectile trajectory correction. Magnetometers have been used for projectile attitude measurement systems as they are small in size, lightweight, and low cost. However, magnetometers are seriously disturbed by the artillery magnetic field during launch. Moreover, the error parameters of the magnetometers, which are calibrated in advance, usually change after extended storage. The changed parameters have negative effects on attitude estimation of the projectile. To improve the accuracy of attitude estimation, the magnetometers should be calibrated again before launch or during flight. This paper presents a fast calibration method specific for a spinning projectile. At the launch site, the tri-axial magnetometer is calibrated, the parameters of magnetometer are quickly obtained by optimal ellipsoid fitting based on a least squares criterion. Then, the calibration parameters are used to compensate for magnetometer outputs during flight. The numerical simulation results show that the proposed calibration method can effectively determine zero bias, scale factors, and alignment angle errors. Finally, a semi-physical experimental system was designed to further verify the performance of the calibration method. The results show that pitch angle error reduces from 3.52° to 0.58° after calibration. The roll angle error is reduced from 2.59° to 0.65°. Simulations and experimental results indicate that the accuracy of magnetometer in strap-down spinning projectile has been greatly enhanced, and the attitude estimation errors are reduced after calibration.
Dissertations / Theses on the topic "Indoor magnetometer calibration. system"
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Mohamadabadi, Kaveh. "Anisotropic Magnetoresistance Magnetometer for inertial navigation systems." Phd thesis, Ecole Polytechnique X, 2013. http://tel.archives-ouvertes.fr/tel-00946970.
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This work addresses the relevant errors of the anisotropic magnetoresistance sensor for inertial navigation systems. The manuscript provides resulting guidelines and solution for using the AMR sensors in a robust and appropriate way relative to the applications. New methods also are proposed to improve the performance and, reduce the power requirements and cost design of the magnetometer. The new compensation method is proposed by developing an optimization algorithm. The necessity of the sensor calibration is shown and the source of the errors and compensating model are investigated. Two novel methods of indoor calibration are proposed and examples of operating systems are presented.
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Säll, Joel, and Johnny Merkel. "Indoor Navigation Using Accelerometer and Magnetometer." Thesis, Linköpings universitet, Institutionen för systemteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-71277.
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This project will create a navigation system based on dead reckoning using anaccelerometer and a magnetometer. There have previously been several studiesmade on navigation with accelerometers, magnetometers (electronic compass) andgyros. With these three components it is possible to do positioning and differentkinds of movement analyses. There are several methods for detection of movementand calculation of position. To achieve greater accuracy in these applications,gyros are often used. Compared to magnetometers and accelerometer gyrosconsumes a lot of power. In an embedded system with limited power suppliesfrom a battery this may be unacceptable. In this project a positioning system without a gyro have been developedand evaluated. Is this possible to do, and what accuracy is possible to achieve arequestions asked.Algorithms have been developed and tested in MATLAB. The project is based ona device called a BeeBadge, part of the project will be to transfer the developedalgorithms from MATLAB to C-code. Optimizations of the code will be performeddue to the constraints in the memory and speed of the microcontroller on theBeeBadge.
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Liu, Yang. "An indoor pedestrian localisation system with self-calibration capability." Thesis, University of Sheffield, 2016. http://etheses.whiterose.ac.uk/13474/.
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The Global Positioning System (GPS), a space-based system, employs dozens of satellites to provide location determination and navigation services around the world. However, due to the constraints to the power consuming and long-distance transmission, the strength of the GPS signal received on the mobile device is weak. Errors of the detection of the line-of-sight (LOS) propagated components of the signals are expected to be high if the users are in urban areas or in buildings, since obstacles in the surrounding environments could attenuate the LOS propagated components of the GPS signals significantly, but might upfade the multi-path components (constructive multi-path effect). Therefore, GPS should be replaced by other techniques for providing localisation services in urban areas or, especially, in indoor environments. Among all the candidates, received signal strength (RSS) location fingerprint based positioning systems attract great attentions from both the academia and industry. Usually, a time-consuming and labour-intensive site survey to collect dozens of training samples of RSS from access points (APs) in range on every reference position (RP) in the area of interest is required to build the radio map (RM), before the localisation services could be provided to users. The purpose of the thesis is to reduce the workload involved in the site survey while providing accurate localisation service from two aspects, as shown as follows. Firstly, the quantity of the training samples collected on each RP is reduced, by taking advantage of the on-line RSS measurements collected by users to calibrate the RM. The on-line RSS measurements are geo-tagged probabilistically by an implementation of particle filter to track the trajectories of the users. The employed particles in estimation of the users’ states are initialised by a supervised clustering algorithm, propagated according to the analysis of the data sourcing from inertial measurement units (IMUs), e.g., walking detection, orientation estimation, step and stepping moments detection, step length detection, etc., and corrected by the wall constraints. Furthermore, the importance weights of the particles are adjusted to reduce the negative influence of the multi-clustered distribution of the particles to the on-line localisation accuracy, by applying the on-line RSS-based localisation results when significant users' body turnings are detected. The final results confirm that the accuracy of the localisation service with the RM calibrated by the method proposed in this thesis is higher than the previously proposed approach taking advantage of expectation maximisation algorithm. Secondly, a semi-automatic site-survey method which takes advantage of a route-planning algorithm and a walking detection module to recognise automatically the index of the RP for the current site-survey task, inform the system automatically of the start/end of the process of the task on the current RP and switch automatically to the following RPs on the planned route for the following tasks. In this way, human beings' intervention to the site-survey process is greatly reduced. As a result, the errors made in the site-survey tasks, such as incorrect recognition of the index of the RP for the current task which is highly likely to occur when the technicians get absent-minded in the work, misoperations to start/end of the task for collecting RSS samples on the current RP at wrong time moments, forgetting to notify the system of the fact that the technician has moved on to the next RP, etc., are avoided. The technicians no longer feel bored or anxious in the process of fulfilment of site-survey tasks, and the working efficiency and robustness of the RM could be also improved.
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Mohd, Sabri Roslee. "Design of an adaptive RF fingerprint indoor positioning system." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/31069.
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RF fingerprinting can solve the indoor positioning problem with satisfactory accuracy, but the methodology depends on the so-called radio map calibrated in the offline phase via manual site-survey, which is costly, time-consuming and somewhat error-prone. It also assumes the RF fingerprint’s signal-spatial correlations to remain static throughout the online positioning phase, which generally does not hold in practice. This is because indoor environments constantly experience dynamic changes, causing the radio signal strengths to fluctuate over time, which weakens the signal-spatial correlations of the RF fingerprints. State-of-the-arts have proposed adaptive RF fingerprint methodology capable of calibrating the radio map in real-time and on-demand to address these drawbacks. However, existing implementations are highly server-centric, which is less robust, does not scale well, and not privacy-friendly. This thesis aims to address these drawbacks by exploring the feasibility of implementing an adaptive RF fingerprint indoor positioning system in a distributed and client-centric architecture using only commodity Wi-Fi hardware, so it can seamlessly integrate with existing Wi-Fi network and allow it to offer both networking and positioning services. Such approach has not been explored in previous works, which forms the basis of this thesis’ main contribution. The proposed methodology utilizes a network of distributed location beacons as its reference infrastructure; hence the system is more robust since it does not have any single point-of-failure. Each location beacon periodically broadcasts its coordinate to announce its presence in the area, plus coefficients that model its real-time RSS distribution around the transmitting antenna. These coefficients are constantly self-calibrated by the location beacon using empirical RSS measurements obtained from neighbouring location beacons in a collaborative fashion, and fitting the values using path loss with log-normal shadowing model as a function of inter-beacon distances while minimizing the error in a least-squared sense. By self-modelling its RSS distribution in real-time, the location beacon becomes aware of its dynamically fluctuating signal levels caused by physical, environmental and temporal characteristics of the indoor environment. The implementation of this self-modelling feature on commodity Wi-Fi hardware is another original contribution of this thesis. Location discovery is managed locally by the clients, which means the proposed system can support unlimited number of client devices simultaneously while also protect user’s privacy because no information is shared with external parties. It starts by listening for beacon frames broadcasted by nearby location beacons and measuring their RSS values to establish the RF fingerprint of the unknown point. Next, it simulates the reference RF fingerprints of predetermined points inside the target area, effectively calibrating the site’s radio map, by computing the RSS values of all detected location beacons using their respective coordinates and path loss coefficients embedded inside the received beacon frames. Note that the coefficients model the real-time RSS distribution of each location beacon around its transmitting antenna; hence, the radio map is able to adapt itself to the dynamic fluctuations of the radio signal to maintain its signal-spatial correlations. The final step is to search the radio map to find the reference RF fingerprint that most closely resembles the unknown sample, where its coordinate is returned as the location result. One positioning approach would be to first construct a full radio map by computing the RSS of all detected location beacons at all predetermined calibration points, then followed by an exhaustive search over all reference RF fingerprints to find the best match. Generally, RF fingerprint algorithm performs better with higher number of calibration points per unit area since more locations can be classified, while extra RSS components can help to better distinguish between nearby calibration points. However, to calibrate and search many RF fingerprints will incur substantial computing costs, which is unsuitable for power and resource limited client devices. To address this challenge, this thesis introduces a novel algorithm suitable for client-centric positioning as another contribution. Given an unknown RF fingerprint to solve for location, the proposed algorithm first sorts the RSS in descending order. It then iterates over this list, first selecting the location beacon with the strongest RSS because this implies the unknown location is closest to the said location beacon. Next, it computes the beacon’s RSS using its path loss coefficients and coordinate information one calibration point at a time while simultaneously compares the result with the measured value. If they are similar, the algorithm keeps this location for subsequent processing; else it is removed because distant points relative to the unknown location would exhibit vastly different RSS values due to the different site-specific obstructions encountered by the radio signal propagation. The algorithm repeats the process by selecting the next strongest location beacon, but this time it only computes its RSS for those points identified in the previous iteration. After the last iteration completes, the average coordinate of remaining calibration points is returned as the location result. Matlab simulation shows the proposed algorithm only takes about half of the time to produce a location estimate with similar positioning accuracy compared to conventional algorithm that does a full radio map calibration and exhaustive RF fingerprint search. As part of the thesis’ contribution, a prototype of the proposed indoor positioning system is developed using only commodity Wi-Fi hardware and open-source software to evaluate its usability in real-world settings and to demonstrate possible implementation on existing Wi-Fi installations. Experimental results verify the proposed system yields consistent positioning accuracy, even in highly dynamic indoor environments and changing location beacon topologies.
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Chang, Dun-Yuan, and 鄭惇元. "WLAN Smart Antenna System Implement - Beam Calibration and Indoor Beamforming Optimization." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/7uf7uh.
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碩士元智大學
通訊工程學系
106
The establishment of a smart antenna system is mainly composed of a phase control circuit with a phased array antenna. By integrating different software programs with a rewritable microprocessor controller Create different application methods to develop of beam calibration measurement and indoor beamforming optimization . This thesis studies develop two different smart antenna system applications at 2.4 GHz. The goal of the developed are reduced the manual calibration time and increased the performance of the phase array antenna. Another application is to integrate the smart antenna system into the Internet, Wifi, and cloud server. User will upload the usage status to the cloud server through the Internet. Through data reporting, the cloud algorithm calculates the coverage of the antenna so that Wifi signal strength of user is in the best condition.
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Shih-ChiHsiao and 蕭世奇. "Database Calibration using Spatial Interpolation Methods and the Position Error Bounding for Real Time Indoor Positioning System." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/58520142170147481352.
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碩士國立成功大學
航空太空工程學系碩博士班
101
Nowadays, there is a great need for real time indoor positioning systems of mobile users, since the well-developed MEMS technology and there are many kinds of mobile communications devices. It seems that Location Based Service is getting popular to people. Therefore, a low cost and low power consumption Real Time Indoor Positioning System (RTIPS) that is integrated with a self-developed indoor Geographic Information System (GIS) has been developed by Hsu, et al of National Cheng Kung University (NCKU). In order to make RTIPS more flexible to applications, this work tries to implement RTIPS to a larger indoor environment. Once the application location is larger, the challenge we meet is the positioning database calibration because the costs related to time and labor for calibrating a wide area are much higher than those for a smaller site. The purpose of this thesis is to achieve a more available system for an indoor positioning system; thus, this thesis extends the study on the database calibration algorithms of a fingerprint positioning algorithm. In the database calibration stage, the collected signal quality might be affected if the positioning space is geometrically complicated. Thus, the signal transmission paths will be more complicated as well. As a result, we place an emphasis on utilizing two spatial interpolation methods including: 1) the Inverse Distance Weighting (IDW) method; 2) the Kriging method, that are used to yield a denser database from the raw data measurements. Additionally, another purpose of this thesis is to establish a procedure to determine the parameters of the theoretical models since most researches mentioned the parameters regarding as the “sill” and “range” estimation of a semi-variogram that usually depend on a trial and error approach, which consumes a great deal of time. We also conduct a study to investigate the optimal sensor numbers for different indoor environments for users. Finally, in order to make this system more complete, we determine the confidence bound of the positioning result by using the estimation variance provided by the Kriging method through the interpolation process. Moreover, we modify the estimation variance to define and calculate the confidence bound for indoor positioning, to provide users with a more safe indoor positioning service.
Book chapters on the topic "Indoor magnetometer calibration. system"
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Troll, Péter, Károly Szipka, and Andreas Archenti. "Indoor Localization of Quadcopters in Industrial Environment." In Advances in Transdisciplinary Engineering. IOS Press, 2020. http://dx.doi.org/10.3233/atde200183.
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The research work in this paper was carried out to reach advanced positioning capabilities of unmanned aerial vehicles (UAVs) for indoor applications. The paper includes the design of a quadcopter and the implementation of a control system with the capability to position the quadcopter indoor using onboard visual pose estimation system, without the help of GPS. The project also covered the design and implementation of quadcopter hardware and the control software. The developed hardware enables the quadcopter to raise at least 0.5kg additional payload. The system was developed on a Raspberry single-board computer in combination with a PixHawk flight controller. OpenCV library was used to implement the necessary computer vision. The Open-source software-based solution was developed in the Robotic Operating System (ROS) environment, which performs sensor reading and communication with the flight controller while recording data about its operation and transmits those to the user interface. For the vision-based position estimation, pre-positioned printed markers were used. The markers were generated by ArUco coding, which exactly defines the current position and orientation of the quadcopter, with the help of computer vision. The resulting data was processed in the ROS environment. LiDAR with Hector SLAM algorithm was used to map the objects around the quadcopter. The project also deals with the necessary camera calibration. The fusion of signals from the camera and from the IMU (Inertial Measurement Unit) was achieved by using Extended Kalman Filter (EKF). The evaluation of the completed positioning system was performed with an OptiTrack optical-based external multi-camera measurement system. The introduced evaluation method has enough precision to be used to investigate the enhancement of positioning performance of quadcopters, as well as fine-tuning the parameters of the used controller and filtering approach. The payload capacity allows autonomous material handling indoors. Based on the experiments, the system has an accurate positioning system to be suitable for industrial application.
Conference papers on the topic "Indoor magnetometer calibration. system"
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Poulose, Alwin, Jihun Kim, and Dong Seog Han. "Indoor Localization with Smartphones: Magnetometer Calibration." In 2019 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2019. http://dx.doi.org/10.1109/icce.2019.8661986.
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Hellmers, Hendrik, Abdelmoumen Norrdine, Jorg Blankenbach, and Andreas Eichhorn. "An IMU/magnetometer-based Indoor positioning system using Kalman filtering." In 2013 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2013. http://dx.doi.org/10.1109/ipin.2013.6817887.
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Busato, Ariela, Pavel Paces, and Jan Popelka. "Magnetometer data fusion algorithms performance in indoor navigation: Comparison, calibration and testing." In 2014 IEEE Metrology for Aerospace (MetroAeroSpace). IEEE, 2014. http://dx.doi.org/10.1109/metroaerospace.2014.6865955.
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Leplawy, Marcin, Piotr Lipinski, and Krzysztof Lichy. "Hybrid localization indoor system using WiFi and magnetometer sensor." In 2017 18th International Symposium on Electromagnetic Fields in Mechatronics, Electrical and Electronic Engineering (ISEF). IEEE, 2017. http://dx.doi.org/10.1109/isef.2017.8090723.
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Park, P., W. Kim, S. M. Amran, and V. Y. Shifrin. "Calibration system for magnetometer in low magnetic field range." In 2017 IEEE International Magnetics Conference (INTERMAG). IEEE, 2017. http://dx.doi.org/10.1109/intmag.2017.8007974.
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Yan, Rui, Fubin Zhang, and Huihui Chen. "A MEMS-based Magnetometer Calibration Approach in AUV Navigation System." In OCEANS 2019 - Marseille. IEEE, 2019. http://dx.doi.org/10.1109/oceanse.2019.8867368.
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Koppe, Enrico, Daniel Augustin, Achim Liers, and Jochen Schiller. "Automatic 3D calibration for a multi-sensor system." In 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2012. http://dx.doi.org/10.1109/ipin.2012.6418870.
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Vaupel, Thorsten, Jochen Seitz, Frederic Kiefer, Stephan Haimerl, and Jorn Thielecke. "Wi-Fi positioning: System considerations and device calibration." In 2010 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2010. http://dx.doi.org/10.1109/ipin.2010.5646207.
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Petrucha, Vojtech, and Petr Kaspar. "Calibration of a triaxial fluxgate magnetometer and accelerometer with an automated non-magnetic calibration system." In 2009 IEEE Sensors. IEEE, 2009. http://dx.doi.org/10.1109/icsens.2009.5398466.
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Selmi, Ikhlas, Alexandre Vervisch-Picois, Yaneck Gottesman, and Nel Samama. "Optical and radio calibration of the repealite based indoor positioning system." In 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2012. http://dx.doi.org/10.1109/ipin.2012.6418906.
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