Details
| Original language | English |
|---|---|
| Title of host publication | 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE) |
| Pages | 1132-1139 |
| Number of pages | 8 |
| ISBN (electronic) | 9781665418737 |
| Publication status | Published - 2021 |
| Event | 17th International Conference on Automation Science and Engineering (CASE) - Lyon, France Duration: 23 Aug 2021 → 27 Aug 2021 Conference number: 17 |
Publication series
| Name | IEEE International Conference on Automation Science and Engineering |
|---|---|
| Volume | 2021-August |
| ISSN (Print) | 2161-8070 |
| ISSN (electronic) | 2161-8089 |
Abstract
Taking advantage of the complementary error characteristics of Light Detection and Ranging (LiDAR) and stereo camera reconstruction, we propose a set-membership-based method for fusing LiDAR information with dense stereo data under consideration of interval uncertainty of all measurements and calibration parameters. Employing interval analysis, we can propagate the uncertainties to the extraction of distinct features in a straightforward manner. To show the applicability of our approach, we use the fused information for dead reckoning. In contrast to other works, we can consistently propagate the sensor uncertainties to the localization of the robot. Further, we can provide guaranteed bounds for the relative motion between consecutive frames. Using real data we validate that our approach is indeed able to always enclose the true pose of the robot.
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Electrical and Electronic Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
2021 IEEE 17th International Conference on Automation Science and Engineering (CASE). 2021. p. 1132-1139 (IEEE International Conference on Automation Science and Engineering; Vol. 2021-August).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Stereo-Visual-LiDAR Sensor Fusion Using Set-Membership Methods
AU - Ehambram, Aaronkumar
AU - Voges, Raphael
AU - Wagner, Bernardo
N1 - Conference code: 17
PY - 2021
Y1 - 2021
N2 - Taking advantage of the complementary error characteristics of Light Detection and Ranging (LiDAR) and stereo camera reconstruction, we propose a set-membership-based method for fusing LiDAR information with dense stereo data under consideration of interval uncertainty of all measurements and calibration parameters. Employing interval analysis, we can propagate the uncertainties to the extraction of distinct features in a straightforward manner. To show the applicability of our approach, we use the fused information for dead reckoning. In contrast to other works, we can consistently propagate the sensor uncertainties to the localization of the robot. Further, we can provide guaranteed bounds for the relative motion between consecutive frames. Using real data we validate that our approach is indeed able to always enclose the true pose of the robot.
AB - Taking advantage of the complementary error characteristics of Light Detection and Ranging (LiDAR) and stereo camera reconstruction, we propose a set-membership-based method for fusing LiDAR information with dense stereo data under consideration of interval uncertainty of all measurements and calibration parameters. Employing interval analysis, we can propagate the uncertainties to the extraction of distinct features in a straightforward manner. To show the applicability of our approach, we use the fused information for dead reckoning. In contrast to other works, we can consistently propagate the sensor uncertainties to the localization of the robot. Further, we can provide guaranteed bounds for the relative motion between consecutive frames. Using real data we validate that our approach is indeed able to always enclose the true pose of the robot.
UR - http://www.scopus.com/inward/record.url?scp=85117044055&partnerID=8YFLogxK
U2 - 10.1109/case49439.2021.9551516
DO - 10.1109/case49439.2021.9551516
M3 - Conference contribution
SN - 978-1-6654-4809-3
T3 - IEEE International Conference on Automation Science and Engineering
SP - 1132
EP - 1139
BT - 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)
T2 - 17th International Conference on Automation Science and Engineering (CASE)
Y2 - 23 August 2021 through 27 August 2021
ER -