Redesigning SLAM for Arbitrary Multi-Camera Systems
arXiv:2003.02014 · doi:10.1109/ICRA40945.2020.9197553
Abstract
Adding more cameras to SLAM systems improves robustness and accuracy but complicates the design of the visual front-end significantly. Thus, most systems in the literature are tailored for specific camera configurations. In this work, we aim at an adaptive SLAM system that works for arbitrary multi-camera setups. To this end, we revisit several common building blocks in visual SLAM. In particular, we propose an adaptive initialization scheme, a sensor-agnostic, information-theoretic keyframe selection algorithm, and a scalable voxel-based map. These techniques make little assumption about the actual camera setups and prefer theoretically grounded methods over heuristics. We adapt a state-of-the-art visual-inertial odometry with these modifications, and experimental results show that the modified pipeline can adapt to a wide range of camera setups (e.g., 2 to 6 cameras in one experiment) without the need of sensor-specific modifications or tuning.
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Cited by in corpus (6)
- Balancing the Budget: Feature Selection and Tracking for Multi-Camera Visual-Inertial Odometry
- Design and Evaluation of a Generic Visual SLAM Framework for Multi-Camera Systems
- BAMF-SLAM: Bundle Adjusted Multi-Fisheye Visual-Inertial SLAM Using Recurrent Field Transforms
- A Model for Multi-View Residual Covariances based on Perspective Deformation
- Rendering the Directional TSDF for Tracking and Multi-Sensor Registration with Point-To-Plane Scale ICP
- Learning Camera Performance Models for Active Multi-Camera Visual Teach and Repeat