Pub Date : 2020-09-14DOI: 10.1109/MFI49285.2020.9235214
Philipp Berthold, Martin Michaelis, T. Luettel, D. Meissner, H. Wuensche
One major challenge in extended object tracking is the association of a point measurement to its true origin on a target object. The origins of measurements are often spatially distributed over the full extent of the target. The association of measurements to the possible origins within the targets’ extent is difficult, especially for low-resolution sensors which provide only a few measurements per object. We address this using a soft association of a point measurement to its origin candidates on the target. Therefore, association probabilities to different possible origins are calculated for each measurement. These probabilities are weighted according to their probability in the filtering step. We also extend this filter to continuous and not just discrete association possibilities. This allows us to associate point measurements to lines.This paper outlines the derivation of the filter and gives three exemplary applications. A simulation compares the performance of this approach with other filter techniques for tracking a moving line. The transfer of the filter to a moving circle is discussed. Additionally, we discuss its usage for a Doppler-radar-based detection association which exploits the radial speed information. We discuss the advantages and the drawbacks of this approach and give recommendations for the optimization of computation time.
{"title":"A Continuous Probabilistic Origin Association Filter for Extended Object Tracking","authors":"Philipp Berthold, Martin Michaelis, T. Luettel, D. Meissner, H. Wuensche","doi":"10.1109/MFI49285.2020.9235214","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235214","url":null,"abstract":"One major challenge in extended object tracking is the association of a point measurement to its true origin on a target object. The origins of measurements are often spatially distributed over the full extent of the target. The association of measurements to the possible origins within the targets’ extent is difficult, especially for low-resolution sensors which provide only a few measurements per object. We address this using a soft association of a point measurement to its origin candidates on the target. Therefore, association probabilities to different possible origins are calculated for each measurement. These probabilities are weighted according to their probability in the filtering step. We also extend this filter to continuous and not just discrete association possibilities. This allows us to associate point measurements to lines.This paper outlines the derivation of the filter and gives three exemplary applications. A simulation compares the performance of this approach with other filter techniques for tracking a moving line. The transfer of the filter to a moving circle is discussed. Additionally, we discuss its usage for a Doppler-radar-based detection association which exploits the radial speed information. We discuss the advantages and the drawbacks of this approach and give recommendations for the optimization of computation time.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114999843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-14DOI: 10.1109/MFI49285.2020.9235242
Leticia González, J. C. Álvarez, Antonio M. López, D. Álvarez
This article describes a new methodology for the metrological evaluation of a human-robot collaborative environment based on optical motion capture (OMC) systems. By taking advantage of the existing industrial robot in the production cell, the workspace calibration procedure can be automatized, reducing the need of human intervention. The method is inspired on the ASTM E3064 test guide, and the results presented show that the metrological characteristics so obtained are compatible and comparable in quality to the ones with the manual procedure.
{"title":"Evaluation of optical motion capture system performance in humanrobot collaborative cells","authors":"Leticia González, J. C. Álvarez, Antonio M. López, D. Álvarez","doi":"10.1109/MFI49285.2020.9235242","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235242","url":null,"abstract":"This article describes a new methodology for the metrological evaluation of a human-robot collaborative environment based on optical motion capture (OMC) systems. By taking advantage of the existing industrial robot in the production cell, the workspace calibration procedure can be automatized, reducing the need of human intervention. The method is inspired on the ASTM E3064 test guide, and the results presented show that the metrological characteristics so obtained are compatible and comparable in quality to the ones with the manual procedure.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122305024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-14DOI: 10.1109/MFI49285.2020.9235223
Aquib Rashid, Kannan Peesapati, M. Bdiwi, Sebastian Krusche, W. Hardt, M. Putz
Implementation of safe and efficient human robot collaboration for agile production cells with heavy-duty industrial robots, having large stopping distances and large self-occlusion areas, is a challenging task. Collision avoidance is the main functionality required to realize this task. In fact, it requires accurate estimation of shortest distance between known (robot) and unknown (human or anything else) objects in a large area. This work proposes a selective fusion of global and local sensors, representing a large range 360° LiDAR and a small range RGB camera respectively, in the context of dynamic speed and separation monitoring. Safety functionality has been evaluated for collision detection between unknown dynamic object to manipulator joints. The system yields 29-40% efficiency compared to fenced system. Heavy-duty industrial robot and a controlled linear axis dummy is used for evaluating different robot and scenario configurations. Results suggest higher efficiency and safety when using local and global setup.
{"title":"Local and Global Sensors for Collision Avoidance","authors":"Aquib Rashid, Kannan Peesapati, M. Bdiwi, Sebastian Krusche, W. Hardt, M. Putz","doi":"10.1109/MFI49285.2020.9235223","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235223","url":null,"abstract":"Implementation of safe and efficient human robot collaboration for agile production cells with heavy-duty industrial robots, having large stopping distances and large self-occlusion areas, is a challenging task. Collision avoidance is the main functionality required to realize this task. In fact, it requires accurate estimation of shortest distance between known (robot) and unknown (human or anything else) objects in a large area. This work proposes a selective fusion of global and local sensors, representing a large range 360° LiDAR and a small range RGB camera respectively, in the context of dynamic speed and separation monitoring. Safety functionality has been evaluated for collision detection between unknown dynamic object to manipulator joints. The system yields 29-40% efficiency compared to fenced system. Heavy-duty industrial robot and a controlled linear axis dummy is used for evaluating different robot and scenario configurations. Results suggest higher efficiency and safety when using local and global setup.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122844294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-14DOI: 10.1109/MFI49285.2020.9235232
Tom L. Koller, U. Frese
The interacting multiple model filter is the standard in state estimation where different dynamic models are required to model the behavior of a system. It performs a probabilistic mixing of estimates. Up to now, it is undefined how to perform this mixing properly on manifold spaces, e.g. quaternions. We present the proper probabilistic mixing on differentiable manifolds based on the boxplus-method. The result is the interacting multiple model filter on boxplus-manifolds. We prove that our approach is a first order correct approximation of the optimum. The approach is evaluated in a simulation and performs as good as the ad-hoc solution for quaternions. A generic implementation of the boxplus interacting multiple model filter for differentiable manifolds is published alongside with this paper.
{"title":"The Interacting Multiple Model Filter on Boxplus-Manifolds","authors":"Tom L. Koller, U. Frese","doi":"10.1109/MFI49285.2020.9235232","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235232","url":null,"abstract":"The interacting multiple model filter is the standard in state estimation where different dynamic models are required to model the behavior of a system. It performs a probabilistic mixing of estimates. Up to now, it is undefined how to perform this mixing properly on manifold spaces, e.g. quaternions. We present the proper probabilistic mixing on differentiable manifolds based on the boxplus-method. The result is the interacting multiple model filter on boxplus-manifolds. We prove that our approach is a first order correct approximation of the optimum. The approach is evaluated in a simulation and performs as good as the ad-hoc solution for quaternions. A generic implementation of the boxplus interacting multiple model filter for differentiable manifolds is published alongside with this paper.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122584058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-14DOI: 10.1109/MFI49285.2020.9235221
Luisa Still, M. Oispuu
This paper investigates the problem of shooter localization fusing complete or incomplete experimental data of one or multiple acoustic sensors. A microphone array can measure a complete measurement data set, composed of two bearing angles of the two impulsive sound events of a supersonic bullet and the TDOA between both events, or an incomplete subset. In this paper experimental results from a field experiment with volumetric microphone arrays are investigated and compared with the associated Cramér-Rao bound.
{"title":"Field Experiments on Shooter State Estimation Accuracy Based on Incomplete Acoustic Measurements","authors":"Luisa Still, M. Oispuu","doi":"10.1109/MFI49285.2020.9235221","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235221","url":null,"abstract":"This paper investigates the problem of shooter localization fusing complete or incomplete experimental data of one or multiple acoustic sensors. A microphone array can measure a complete measurement data set, composed of two bearing angles of the two impulsive sound events of a supersonic bullet and the TDOA between both events, or an incomplete subset. In this paper experimental results from a field experiment with volumetric microphone arrays are investigated and compared with the associated Cramér-Rao bound.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117039882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-14DOI: 10.1109/MFI49285.2020.9235212
Daniel Frisch, U. Hanebeck
We propose a fast method for deterministic multi-variate Gaussian sampling. In many application scenarios, the commonly used stochastic Gaussian sampling could simply be replaced by our method – yielding comparable results with a much smaller number of samples. Conformity between the reference Gaussian density function and the distribution of samples is established by minimizing a distance measure between Gaussian density and Dirac mixture density. A modified Cramér-von Mises distance of the Localized Cumulative Distributions (LCDs) of the two densities is employed that allows a direct comparison between continuous and discrete densities in higher dimensions. Because numerical minimization of this distance measure is not feasible under real time constraints, we propose to build a library that maintains sample locations from the standard normal distribution as a template for each number of samples in each dimension. During run time, the requested sample set is re-scaled according to the eigenvalues of the covariance matrix, rotated according to the eigenvectors, and translated according to the mean vector, thus adequately representing arbitrary multivariate normal distributions.
{"title":"Efficient Deterministic Conditional Sampling of Multivariate Gaussian Densities","authors":"Daniel Frisch, U. Hanebeck","doi":"10.1109/MFI49285.2020.9235212","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235212","url":null,"abstract":"We propose a fast method for deterministic multi-variate Gaussian sampling. In many application scenarios, the commonly used stochastic Gaussian sampling could simply be replaced by our method – yielding comparable results with a much smaller number of samples. Conformity between the reference Gaussian density function and the distribution of samples is established by minimizing a distance measure between Gaussian density and Dirac mixture density. A modified Cramér-von Mises distance of the Localized Cumulative Distributions (LCDs) of the two densities is employed that allows a direct comparison between continuous and discrete densities in higher dimensions. Because numerical minimization of this distance measure is not feasible under real time constraints, we propose to build a library that maintains sample locations from the standard normal distribution as a template for each number of samples in each dimension. During run time, the requested sample set is re-scaled according to the eigenvalues of the covariance matrix, rotated according to the eigenvectors, and translated according to the mean vector, thus adequately representing arbitrary multivariate normal distributions.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129573578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-14DOI: 10.1109/MFI49285.2020.9235226
Jasprabhjit Mehami, Teresa Vidal-Calleja, A. Alempijevic
Multi-modal sensors such as hyperspectral line-scan and frame cameras can be incorporated into a single camera system, enabling individual sensor limitations to be compensated. Calibration of such systems is crucial to ensure data from one modality can be related to the other. The best known approach is to capture multiple measurements of a known planar pattern, which are then used to optimize calibration parameters through non-linear least squares. The confidence in the optimized parameters is dependent on the measurements, which are contaminated by noise due to sensor hardware. Understanding how this noise transfers through the calibration is essential, especially when dealing with line-scan cameras that rely on measurements to extract feature points. This paper adopts a maximum likelihood estimation method for propagating measurement noise through the calibration, such that the optimized parameters are associated with an estimate of uncertainty. The uncertainty enables development of an active calibration algorithm, which uses observability to selectively choose images that improve parameter estimation. The algorithm is tested in both simulation and hardware, then compared to a naive approach that uses all images to calibrate. The simulation results for the algorithm show a drop of 26.4% in the total normalized error and 46.8% in the covariance trace. Results from the hardware experiments also show a decrease in the covariance trace, demonstrating the importance of selecting good measurements for parameter estimation.
{"title":"Observability driven Multi-modal Line-scan Camera Calibration","authors":"Jasprabhjit Mehami, Teresa Vidal-Calleja, A. Alempijevic","doi":"10.1109/MFI49285.2020.9235226","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235226","url":null,"abstract":"Multi-modal sensors such as hyperspectral line-scan and frame cameras can be incorporated into a single camera system, enabling individual sensor limitations to be compensated. Calibration of such systems is crucial to ensure data from one modality can be related to the other. The best known approach is to capture multiple measurements of a known planar pattern, which are then used to optimize calibration parameters through non-linear least squares. The confidence in the optimized parameters is dependent on the measurements, which are contaminated by noise due to sensor hardware. Understanding how this noise transfers through the calibration is essential, especially when dealing with line-scan cameras that rely on measurements to extract feature points. This paper adopts a maximum likelihood estimation method for propagating measurement noise through the calibration, such that the optimized parameters are associated with an estimate of uncertainty. The uncertainty enables development of an active calibration algorithm, which uses observability to selectively choose images that improve parameter estimation. The algorithm is tested in both simulation and hardware, then compared to a naive approach that uses all images to calibrate. The simulation results for the algorithm show a drop of 26.4% in the total normalized error and 46.8% in the covariance trace. Results from the hardware experiments also show a decrease in the covariance trace, demonstrating the importance of selecting good measurements for parameter estimation.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114708453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-14DOI: 10.1109/MFI49285.2020.9235255
J. Honer, Hauke Kaulbersch
We apply the concept of random set cluster processes in combination with a learned measurement model to extended target tracking. The spatial distribution of measurements generated by a target vehicle is learned via a variational Gaussian mixture (VGM) model. The VGM is then interpreted as the measurement likelihood of a Multi-Bernoulli (MB) distribution. We derive a closed-form Bayesian recursion for tracking an extended target by the use of random set cluster process. This formulation is particularly successful for sparse and noisy measurements, and is applied to automotive Radio Detection and Ranging (RADAR) detections. Last, we provide a large-scale evaluation of our approach based on the data published in the Nuscenes data set.
{"title":"Bayesian Extended Target Tracking with Automotive Radar using Learned Spatial Distribution Models","authors":"J. Honer, Hauke Kaulbersch","doi":"10.1109/MFI49285.2020.9235255","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235255","url":null,"abstract":"We apply the concept of random set cluster processes in combination with a learned measurement model to extended target tracking. The spatial distribution of measurements generated by a target vehicle is learned via a variational Gaussian mixture (VGM) model. The VGM is then interpreted as the measurement likelihood of a Multi-Bernoulli (MB) distribution. We derive a closed-form Bayesian recursion for tracking an extended target by the use of random set cluster process. This formulation is particularly successful for sparse and noisy measurements, and is applied to automotive Radio Detection and Ranging (RADAR) detections. Last, we provide a large-scale evaluation of our approach based on the data published in the Nuscenes data set.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127980849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-14DOI: 10.1109/MFI49285.2020.9235258
J. Kent, Shambo Bhattacharjee, W. Faber, I. Hussein
Consider an object in orbit about the earth for which a sequence of angles-only measurements is made. This paper looks in detail at a one-step update for the filtering problem. Although the problem appears very nonlinear at first sight, it can be almost reduced to the standard linear Kalman filter by a careful formulation. The key features of this formulation are (1) the use of a local or adapted basis rather than a fixed basis for three-dimensional Euclidean space and the use of structural rather than ambient coordinates to represent the state, (2) the development of a novel "normal:conditional- normal" distribution to described the propagated position of the state, and (3) the development of a novel "Observation- Centered" Kalman filter to update the state distribution.A major advantage of this unified approach is that it gives a closed form filter which is highly accurate under a wide range of conditions, including high initial uncertainty, high eccentricity and long propagation times.
{"title":"A Unified Approach to The Orbital Tracking Problem","authors":"J. Kent, Shambo Bhattacharjee, W. Faber, I. Hussein","doi":"10.1109/MFI49285.2020.9235258","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235258","url":null,"abstract":"Consider an object in orbit about the earth for which a sequence of angles-only measurements is made. This paper looks in detail at a one-step update for the filtering problem. Although the problem appears very nonlinear at first sight, it can be almost reduced to the standard linear Kalman filter by a careful formulation. The key features of this formulation are (1) the use of a local or adapted basis rather than a fixed basis for three-dimensional Euclidean space and the use of structural rather than ambient coordinates to represent the state, (2) the development of a novel \"normal:conditional- normal\" distribution to described the propagated position of the state, and (3) the development of a novel \"Observation- Centered\" Kalman filter to update the state distribution.A major advantage of this unified approach is that it gives a closed form filter which is highly accurate under a wide range of conditions, including high initial uncertainty, high eccentricity and long propagation times.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122255982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-14DOI: 10.1109/MFI49285.2020.9235259
Takumi Takebayashi, Renato Miyagusuku, K. Ozaki
Localization is one of the most fundamental requirements for the use of autonomous robots. In this work, we use magnetic-based localization; which, while not as accurate as laser rangefinder or camera-based systems, is not affected by a large number of people on its surrounding, making it ideal for applications where this is expected, such as service robotics in supermarkets, hotels, etc. Magnetic-based localization systems first create a magnetic map of the environment using magnetic samples acquired a priori. An approach for generating this map is to use collected data to training a Gaussian Process model. Gaussian Processes are non-parametric, data-drive models, where the most important design choice is the selection of an adequate kernel function. The purpose of this study is to improve the accuracy of the magnetic localization by testing several kernel functions and experimentally verifying its effects on robot localization.
{"title":"Effect of Kernel Function to Magnetic Map and Evaluation of Localization of Magnetic Navigation","authors":"Takumi Takebayashi, Renato Miyagusuku, K. Ozaki","doi":"10.1109/MFI49285.2020.9235259","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235259","url":null,"abstract":"Localization is one of the most fundamental requirements for the use of autonomous robots. In this work, we use magnetic-based localization; which, while not as accurate as laser rangefinder or camera-based systems, is not affected by a large number of people on its surrounding, making it ideal for applications where this is expected, such as service robotics in supermarkets, hotels, etc. Magnetic-based localization systems first create a magnetic map of the environment using magnetic samples acquired a priori. An approach for generating this map is to use collected data to training a Gaussian Process model. Gaussian Processes are non-parametric, data-drive models, where the most important design choice is the selection of an adequate kernel function. The purpose of this study is to improve the accuracy of the magnetic localization by testing several kernel functions and experimentally verifying its effects on robot localization.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"296 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133037137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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