Paper ID: 2304.13182
Multi-Camera Visual-Inertial Simultaneous Localization and Mapping for Autonomous Valet Parking
Marcus Abate, Ariel Schwartz, Xue Iuan Wong, Wangdong Luo, Rotem Littman, Marc Klinger, Lars Kuhnert, Douglas Blue, Luca Carlone
Localization and mapping are key capabilities for self-driving vehicles. In this paper, we build on Kimera and extend it to use multiple cameras as well as external (eg wheel) odometry sensors, to obtain accurate and robust odometry estimates in real-world problems. Additionally, we propose an effective scheme for closing loops that circumvents the drawbacks of common alternatives based on the Perspective-n-Point method and also works with a single monocular camera. Finally, we develop a method for dense 3D mapping of the free space that combines a segmentation network for free-space detection with a homography-based dense mapping technique. We test our system on photo-realistic simulations and on several real datasets collected on a car prototype developed by the Ford Motor Company, spanning both indoor and outdoor parking scenarios. Our multi-camera system is shown to outperform state-of-the art open-source visual-inertial-SLAM pipelines (Vins-Fusion, ORB-SLAM3), and exhibits an average trajectory error under 1% of the trajectory length across more than 8km of distance traveled (combined across all datasets). A video showcasing the system is available at: youtu.be/H8CpzDpXOI8.
Submitted: Apr 25, 2023