Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841330
Leonhard Kunczik, S. Tornow
For quantum classification methods, the quantum kernel has a role in encoding the data set from its original low-dimensional real space into a high-dimensional quantum state space utilizing quantum circuits. Finding quantum kernels that provide an advantage in real-world data classification is a major challenge, especially when dealing with heterogeneous data or a large data set, requiring more qubits than are available on current devices. Here, we propose two new methods: first, we implement a multiple kernel method for data coming from multiple sources, and second, we propose to combine quantum chips to process larger data sets. The latter can be realized by splitting larger quantum circuits into smaller sub-circuits. We experimentally implement a multiple quantum kernel approach for different data sets on IBM quantum computers and benchmark their results.
{"title":"Quantum Kernel Based Data Fusion","authors":"Leonhard Kunczik, S. Tornow","doi":"10.23919/fusion49751.2022.9841330","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841330","url":null,"abstract":"For quantum classification methods, the quantum kernel has a role in encoding the data set from its original low-dimensional real space into a high-dimensional quantum state space utilizing quantum circuits. Finding quantum kernels that provide an advantage in real-world data classification is a major challenge, especially when dealing with heterogeneous data or a large data set, requiring more qubits than are available on current devices. Here, we propose two new methods: first, we implement a multiple kernel method for data coming from multiple sources, and second, we propose to combine quantum chips to process larger data sets. The latter can be realized by splitting larger quantum circuits into smaller sub-circuits. We experimentally implement a multiple quantum kernel approach for different data sets on IBM quantum computers and benchmark their results.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128851214","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841371
I. Lee, Chan-Gook Park
This paper proposes two probability correction functions to make adaptively the transition probability matrix(TPM). In a traditional interacting multiple model(IMM) estimator, TPM is usually considered a constant as initial values, so it is conveniently calculated by fixing prior information. However, inaccurate TPM can result in a large target state estimation error. To solve the problem, The IMM algorithm needs to have a time-varying transition probability so that the system model changes promptly according to the target movement. Therefore, a two-stage correction function is designed according to the period. The first phase is the accumulating transition probability correction function which increases the probability of the model matching the target movement and decreases others when the model jump does not occur. The second phase is the activating transition probability correction function which quickly updates the probability when the model jump occurs. By the performance comparison between the proposed adaptive IMM and the traditional IMM, the effect of probability correction functions is confirmed and the performance is improved.
{"title":"A Two-stage Transition Correction Function for Adaptive Markov Matrix in IMM Algorithm","authors":"I. Lee, Chan-Gook Park","doi":"10.23919/fusion49751.2022.9841371","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841371","url":null,"abstract":"This paper proposes two probability correction functions to make adaptively the transition probability matrix(TPM). In a traditional interacting multiple model(IMM) estimator, TPM is usually considered a constant as initial values, so it is conveniently calculated by fixing prior information. However, inaccurate TPM can result in a large target state estimation error. To solve the problem, The IMM algorithm needs to have a time-varying transition probability so that the system model changes promptly according to the target movement. Therefore, a two-stage correction function is designed according to the period. The first phase is the accumulating transition probability correction function which increases the probability of the model matching the target movement and decreases others when the model jump does not occur. The second phase is the activating transition probability correction function which quickly updates the probability when the model jump occurs. By the performance comparison between the proposed adaptive IMM and the traditional IMM, the effect of probability correction functions is confirmed and the performance is improved.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131857465","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841386
Gianluca Tabella, Yuri Di Martino, D. Ciuonzo, N. Paltrinieri, Xiaodong Wang, P. Rossi
This work tackles the distributed detection & localization of carbon dioxide (CO2) release from storage tanks caused by the opening of pressure relief devices via inexpensive sensor devices in an industrial context. A realistic model of the dispersion is put forward in this paper. Both full-precision and rate-limited setups for sensors are considered, and fusion rules capitalizing the dispersion model are derived. Simulations analyze the performance trends with realistic system parameters (e.g. wind direction).
{"title":"Sensor Fusion for Detection and Localization of Carbon Dioxide Releases for Industry 4.0","authors":"Gianluca Tabella, Yuri Di Martino, D. Ciuonzo, N. Paltrinieri, Xiaodong Wang, P. Rossi","doi":"10.23919/fusion49751.2022.9841386","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841386","url":null,"abstract":"This work tackles the distributed detection & localization of carbon dioxide (CO2) release from storage tanks caused by the opening of pressure relief devices via inexpensive sensor devices in an industrial context. A realistic model of the dispersion is put forward in this paper. Both full-precision and rate-limited setups for sensors are considered, and fusion rules capitalizing the dispersion model are derived. Simulations analyze the performance trends with realistic system parameters (e.g. wind direction).","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123898558","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841332
Jan Krejcí, O. Straka, J. Vyskočil, M. Jirík, Uta Dahmen
This paper deals with the problem of tracking multiple objects, in which each object is known to belong to a unique class. We follow the tracking by detection paradigm and assume that the object detector provides scores in addition to each detection. The problem is tackled as simultaneous classification and tracking using random finite sets. Inspired by the multi-Bernoulli mixture (MBM) filter, we propose a solution to the problem by modifying the target birth process. To simplify the implementation and to mitigate the computational costs, we develop tractable solutions with linear complexity. The algorithms are subsequently used for visual tracking of surgical instruments. As a by-product, we derive the prediction step of the Bernoulli filter using the probability generating functionals (PGFLs).
{"title":"Feature-Based Multi-Object Tracking With Maximally One Object per Class","authors":"Jan Krejcí, O. Straka, J. Vyskočil, M. Jirík, Uta Dahmen","doi":"10.23919/fusion49751.2022.9841332","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841332","url":null,"abstract":"This paper deals with the problem of tracking multiple objects, in which each object is known to belong to a unique class. We follow the tracking by detection paradigm and assume that the object detector provides scores in addition to each detection. The problem is tackled as simultaneous classification and tracking using random finite sets. Inspired by the multi-Bernoulli mixture (MBM) filter, we propose a solution to the problem by modifying the target birth process. To simplify the implementation and to mitigate the computational costs, we develop tractable solutions with linear complexity. The algorithms are subsequently used for visual tracking of surgical instruments. As a by-product, we derive the prediction step of the Bernoulli filter using the probability generating functionals (PGFLs).","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114471045","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841349
Maxime Escourrou, Joelle Al Hage, P. Bonnifait
This paper presents a new decentralized approach for collaborative localization and map update relying on land-marks measurements performed by the robots themselves. The method uses a modified version of the Kalman filter, namely Schmidt Kalman filter that approaches the performance of the optimal centralized Kalman filter without the need to update each robot pose. To deal with data incest and limited communication, the computation of cross-covariance errors between robots must be well managed. Each robot individually updates its own map, the map fusion is performed by using the unweighted Kullback-Leibler Average to keep estimation consistency. The performance of the approach is evaluated in a simulation environment where robots are equipped with odometry and a lidar for exteroceptive perception. The results show that collaboration improves the localization of the robots and the estimation of the map while maintaining consistency.
{"title":"Decentralized Collaborative Localization with Map Update using Schmidt-Kalman Filter","authors":"Maxime Escourrou, Joelle Al Hage, P. Bonnifait","doi":"10.23919/fusion49751.2022.9841349","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841349","url":null,"abstract":"This paper presents a new decentralized approach for collaborative localization and map update relying on land-marks measurements performed by the robots themselves. The method uses a modified version of the Kalman filter, namely Schmidt Kalman filter that approaches the performance of the optimal centralized Kalman filter without the need to update each robot pose. To deal with data incest and limited communication, the computation of cross-covariance errors between robots must be well managed. Each robot individually updates its own map, the map fusion is performed by using the unweighted Kullback-Leibler Average to keep estimation consistency. The performance of the approach is evaluated in a simulation environment where robots are equipped with odometry and a lidar for exteroceptive perception. The results show that collaboration improves the localization of the robots and the estimation of the map while maintaining consistency.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114859543","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841370
Matteo Tesori, G. Battistelli, L. Chisci, A. Farina, Graziano A. Manduzio
This paper deals with motion modeling for 2-dimensional tracking of a maneuvering object. Specifically, a new class of nonlinear dynamic motion models, called Lambda:Omicron, is introduced with the purpose of accurately modeling maneuvers (regarded as variations of speed and turning rate) of the moving object. These models rely on the unicycle navigation model, suitably augmented with two chains of integrators to account for the unknown speed and turning rate command inputs. Quasi-exact time-discretization of the continuous-time Lambda:Omicron models is also carried out to allow their exploitation in nonlinear recursive filters. Simulation experiments are presented to show the effectiveness of the proposed models as compared to state-of-the-art linear and nonlinear motion models for tracking of strongly maneuvering objects.
{"title":"Lambda:Omicron - A new prediction model to track maneuvering objects","authors":"Matteo Tesori, G. Battistelli, L. Chisci, A. Farina, Graziano A. Manduzio","doi":"10.23919/fusion49751.2022.9841370","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841370","url":null,"abstract":"This paper deals with motion modeling for 2-dimensional tracking of a maneuvering object. Specifically, a new class of nonlinear dynamic motion models, called Lambda:Omicron, is introduced with the purpose of accurately modeling maneuvers (regarded as variations of speed and turning rate) of the moving object. These models rely on the unicycle navigation model, suitably augmented with two chains of integrators to account for the unknown speed and turning rate command inputs. Quasi-exact time-discretization of the continuous-time Lambda:Omicron models is also carried out to allow their exploitation in nonlinear recursive filters. Simulation experiments are presented to show the effectiveness of the proposed models as compared to state-of-the-art linear and nonlinear motion models for tracking of strongly maneuvering objects.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117065550","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841240
Martin Larsson, Erik Tegler, Kalle Åström, M. Oskarsson
In this paper, we present a framework for doing localization from distance measurements, given an estimate of the local motion. We show how we can register the local motion of a receiver, to a global coordinate system, using trilateration of given distance measurements from the receivers to senders in known positions. We describe how many different motion models can be formulated within the same type of registration framework, by only changing the transformation group. The registration is based on a test and hypothesis framework, such as RANSAC, and we present novel and fast minimal solvers that can be used to bootstrap such methods. The system is tested on both synthetic and real data with promising results.
{"title":"Trilateration Using Motion Models","authors":"Martin Larsson, Erik Tegler, Kalle Åström, M. Oskarsson","doi":"10.23919/fusion49751.2022.9841240","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841240","url":null,"abstract":"In this paper, we present a framework for doing localization from distance measurements, given an estimate of the local motion. We show how we can register the local motion of a receiver, to a global coordinate system, using trilateration of given distance measurements from the receivers to senders in known positions. We describe how many different motion models can be formulated within the same type of registration framework, by only changing the transformation group. The registration is based on a test and hypothesis framework, such as RANSAC, and we present novel and fast minimal solvers that can be used to bootstrap such methods. The system is tested on both synthetic and real data with promising results.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":" 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120829366","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841244
Wujun Li, K. C. Teh, Desheng Zhang, Xiujuan Lu, Wei Yi, A. Kot
The detection and tracking of dim targets in complex environments show great importance in radar systems. The track-before-detect (TBD) technology has been widely researched in the scenario where the target signal-to-noise ratio (SNR) is low, however, there has been no suitable solution to the trade-off between tracking accuracy and computational complexity during the process of multi-frame joint detection in radar systems. In this paper, we propose an efficient two-stage based multi-frame detection and tracking algorithm in radar systems. The proposed algorithm consists of a low threshold pre-processing stage, and a TBD processor, which searches possible target tracks from multiple scans and declares the final estimated tracks. The proposed algorithm provides an accurate evolution of target states over time in polar coordinates to avoid the performance loss and model mismatch due to the nonlinear conversion in mixed coordinates. In addition, we further propose a greedy-based recursive algorithm to implement fast track formation from the over-threshold multi-frame measurement points. Simulation results show that the proposed method achieves a better detection and tracking performance with a low computational complexity.
{"title":"An Improved Two-Stage Based Multi-frame Track-Before-Detect Algorithm in Radar systems","authors":"Wujun Li, K. C. Teh, Desheng Zhang, Xiujuan Lu, Wei Yi, A. Kot","doi":"10.23919/fusion49751.2022.9841244","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841244","url":null,"abstract":"The detection and tracking of dim targets in complex environments show great importance in radar systems. The track-before-detect (TBD) technology has been widely researched in the scenario where the target signal-to-noise ratio (SNR) is low, however, there has been no suitable solution to the trade-off between tracking accuracy and computational complexity during the process of multi-frame joint detection in radar systems. In this paper, we propose an efficient two-stage based multi-frame detection and tracking algorithm in radar systems. The proposed algorithm consists of a low threshold pre-processing stage, and a TBD processor, which searches possible target tracks from multiple scans and declares the final estimated tracks. The proposed algorithm provides an accurate evolution of target states over time in polar coordinates to avoid the performance loss and model mismatch due to the nonlinear conversion in mixed coordinates. In addition, we further propose a greedy-based recursive algorithm to implement fast track formation from the over-threshold multi-frame measurement points. Simulation results show that the proposed method achieves a better detection and tracking performance with a low computational complexity.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125813859","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841365
Adèle Simon, S. Bech, G. Loquet, Jan Østergaard
In complex sound scenes, where multiple sounds are present around a listener, selective attention to one auditory stream is hypothesized to synchronize low-frequency brain activity with the envelope of the attended streams. Recent research has employed stimulus reconstruction from neural data to decode to which auditory stream a listener is paying attention. This could be used to create an auditory attention decoder (AAD), that could be embedded in smart headphones or hearing aids, that would adapt the sound processing based on the attention of the user. However, most of these studies use full scalp electroencephalogram, which is not suitable for implementations in audio devices. To that aim, a smaller EEG device, with fewer electrodes could be used. In the present study, we explore the performance of an AAD based on a smaller number of electrodes during speech and music listening. Participants were presented with two sounds simultaneously, and where asked to attend to one while ignoring the other, and their cortical response was continuously recorded during the lsitening. Using a greedy approach based on reconstruction accuracy, a subset of EEG electrodes that are optimized for linear stimulus reconstruction were selected. The goal of this study is to explore the performance of a linear AAD when reducing the number of electrodes. Results suggest that four well-selected electrodes can be sufficient for a miniaturized AAD as it performs as well as a 64-channels setup. The channels selected vary depending on the type of sound attended, suggesting that different electrodes placement should be used to decode attention during music listening and speech listening.
{"title":"Electrodes selection for cortical auditory attention decoding with EEG during speech and music listening","authors":"Adèle Simon, S. Bech, G. Loquet, Jan Østergaard","doi":"10.23919/fusion49751.2022.9841365","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841365","url":null,"abstract":"In complex sound scenes, where multiple sounds are present around a listener, selective attention to one auditory stream is hypothesized to synchronize low-frequency brain activity with the envelope of the attended streams. Recent research has employed stimulus reconstruction from neural data to decode to which auditory stream a listener is paying attention. This could be used to create an auditory attention decoder (AAD), that could be embedded in smart headphones or hearing aids, that would adapt the sound processing based on the attention of the user. However, most of these studies use full scalp electroencephalogram, which is not suitable for implementations in audio devices. To that aim, a smaller EEG device, with fewer electrodes could be used. In the present study, we explore the performance of an AAD based on a smaller number of electrodes during speech and music listening. Participants were presented with two sounds simultaneously, and where asked to attend to one while ignoring the other, and their cortical response was continuously recorded during the lsitening. Using a greedy approach based on reconstruction accuracy, a subset of EEG electrodes that are optimized for linear stimulus reconstruction were selected. The goal of this study is to explore the performance of a linear AAD when reducing the number of electrodes. Results suggest that four well-selected electrodes can be sufficient for a miniaturized AAD as it performs as well as a 64-channels setup. The channels selected vary depending on the type of sound attended, suggesting that different electrodes placement should be used to decode attention during music listening and speech listening.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126011823","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841366
Dominik Ernst, H. Alkhatib, I. Neumann, S. Vogel
The usage of light detection and ranging sensors (LiDARs) has grown rapidly in recent years. The ability to directly capture 3D point clouds is a big advantage compared to other visual systems like cameras. One disadvantage is that uncertainty information is difficult to obtain for these systems, although this information is crucial for the decisions based on the measurements. This becomes even more important, when LiDARs are used in conjunction with other sensors in multi-sensor systems (MSS). The sensor data fusion with different sensors requires an extrinsic calibration, which describes the transformation between the LiDAR frame and the body frame of the platform. This can be done utilizing object space information measured by the LiDAR, which is used to infer the origin of the sensor frame based on reference geometries. This process can be used to additionally determine intrinsic parameters of the sensor. Possible intrinsic parameters are corrections for the distance measurements or approximations for the uncertainty of the measurement elements. In this work, the determination of extrinsic and intrinsic parameters is combined for the first time with the approximation of a stochastic model for a multi-beam LiDAR. This is demonstrated on a real-data set of a Velodyne VLP-16, for which the transformation parameters between sensor frame and body frame are determined. Additionally, a distance offset is determined and the variance components are estimated to establish a better approximation for the stochastic model. The impact of the calibration field and choice of positions within this calibration field are shown and discussed. The results are evaluated in a separate experiment using a kinematic MSS.
{"title":"Analysis of Multiple Positions for the Intrinsic and Extrinsic Calibration of a Multi-Beam LiDAR","authors":"Dominik Ernst, H. Alkhatib, I. Neumann, S. Vogel","doi":"10.23919/fusion49751.2022.9841366","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841366","url":null,"abstract":"The usage of light detection and ranging sensors (LiDARs) has grown rapidly in recent years. The ability to directly capture 3D point clouds is a big advantage compared to other visual systems like cameras. One disadvantage is that uncertainty information is difficult to obtain for these systems, although this information is crucial for the decisions based on the measurements. This becomes even more important, when LiDARs are used in conjunction with other sensors in multi-sensor systems (MSS). The sensor data fusion with different sensors requires an extrinsic calibration, which describes the transformation between the LiDAR frame and the body frame of the platform. This can be done utilizing object space information measured by the LiDAR, which is used to infer the origin of the sensor frame based on reference geometries. This process can be used to additionally determine intrinsic parameters of the sensor. Possible intrinsic parameters are corrections for the distance measurements or approximations for the uncertainty of the measurement elements. In this work, the determination of extrinsic and intrinsic parameters is combined for the first time with the approximation of a stochastic model for a multi-beam LiDAR. This is demonstrated on a real-data set of a Velodyne VLP-16, for which the transformation parameters between sensor frame and body frame are determined. Additionally, a distance offset is determined and the variance components are estimated to establish a better approximation for the stochastic model. The impact of the calibration field and choice of positions within this calibration field are shown and discussed. The results are evaluated in a separate experiment using a kinematic MSS.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130210148","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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