Details
| Original language | English |
|---|---|
| Title of host publication | 2022 25th International Conference on Information Fusion, FUSION 2022 |
| Number of pages | 8 |
| ISBN (Electronic) | 978-1-7377497-2-1 |
| Publication status | Published - 9 Aug 2022 |
| Event | 2022 25th International Conference on Information Fusion (FUSION) - Linköping Concert and Congress center, Linköping, Sweden Duration: 4 Jul 2022 → 7 Jul 2022 https://www.fusion2022.se/ |
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.
Keywords
- calibration, multi-sensor system, data fusion, parameter estimation, variance component estimation, LiDAR
ASJC Scopus subject areas
- Decision Sciences(all)
- Information Systems and Management
- Computer Science(all)
- Information Systems
- Computer Science(all)
- Signal Processing
- Computer Science(all)
- Computer Vision and Pattern Recognition
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2022 25th International Conference on Information Fusion, FUSION 2022. 2022.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Analysis of Multiple Positions for the Intrinsic and Extrinsic Calibration of a Multi-Beam LiDAR
AU - Ernst, Dominik
AU - Vogel, Sören
AU - Alkhatib, Hamza
AU - Neumann, Ingo
N1 - Funding Information: This work was funded by the German Research Foundation (DFG) as part of the Research Training Group i.c.sens [RTG 2159] and NE 1453/5-1.
PY - 2022/8/9
Y1 - 2022/8/9
N2 - 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.
AB - 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.
KW - calibration
KW - multi-sensor system
KW - data fusion
KW - parameter estimation
KW - variance component estimation
KW - LiDAR
UR - http://www.scopus.com/inward/record.url?scp=85136578828&partnerID=8YFLogxK
U2 - 10.23919/FUSION49751.2022.9841366
DO - 10.23919/FUSION49751.2022.9841366
M3 - Conference contribution
SN - 978-1-6654-8941-6
BT - 2022 25th International Conference on Information Fusion, FUSION 2022
T2 - 2022 25th International Conference on Information Fusion (FUSION)
Y2 - 4 July 2022 through 7 July 2022
ER -