Journal
IEEE ROBOTICS AND AUTOMATION LETTERS
Volume 8, Issue 4, Pages 2102-2109Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2023.3246390
Keywords
Laser radar; Feature extraction; Optimization; Costs; Robot kinematics; Source coding; Octrees; Localization; mapping; sensor fusion
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This paper proposes an octree-based global map of multi-scale surfels that can be updated incrementally. It also introduces a point-to-surfel association scheme and continuous-time optimization to achieve Lidar-Inertial continuous-time odometry and mapping. Experimental results demonstrate the advantages of this system compared to other state-of-the-art methods.
While feature association to a global map has significant benefits, to keep the computations from growing exponentially, most lidar-based odometry and mapping methods opt to associate features with local maps at one voxel scale. Taking advantage of the fact that surfels (surface elements) at different voxel scales can be organized in a tree-like structure, we propose an octree-based global map of multi-scale surfels that can be updated incrementally. This alleviates the need for recalculating, for example, a k-d tree of the whole map repeatedly. The system can also take input from a single or a number of sensors, reinforcing the robustness in degenerate cases. We also propose a point-to-surfel (PTS) association scheme, continuous-time optimization on PTS and IMU preintegration factors, along with loop closure and bundle adjustment, making a complete framework for Lidar-Inertial continuous-time odometry and mapping. Experiments on public and in-house datasets demonstrate the advantages of our system compared to other state-of-the-art methods.
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