Stereo-Visual-LiDAR Sensor Fusion Using Set-Membership Methods
Aaronkumar Ehambram, Raphael Voges, Bernardo Wagner · 2021
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.