2D point cloud registration speeds up robot laser mapping
Unsigned Distance Maps on 2D Point Cloud Registration
Robotics
Summary
Registering 2D point clouds helps robots understand their surroundings by matching points from laser scans. The authors propose a method that stores distances to the nearest points in a grid before matching, so the robot can quickly find correspondences without searching every time. This approach uses math optimization on 2D movements to improve matching even with sensor noise. Their experiments show this method performs well on simulated and real datasets, making laser mapping more efficient and accurate.
What this means in practice
- •For robot developers: Implement faster 2D laser scan alignment in robot localization systems by using precomputed distance maps instead of real-time nearest neighbor searches.
- •For autonomous vehicle engineers: Improve 2D LiDAR-based odometry accuracy in mapping and navigation with a registration approach that better handles sensor noise.
Authors
Ricardo B. Sousa, Giorgio Grisetti, Héber Miguel Sobreira, Carlos André Silva, António Paulo Moreira
Abstract
2D point cloud registration arises in laser odometry and Simultaneous Localization and Mapping (SLAM) for mobile robots. Iterative Closest Point (ICP) is one of the most widely used approaches. Still, its iterative procedure recomputes correspondences via nearest-neighbor search at every iteration, whereas correspondence-free alternatives focus on scan-to-map alignment. This paper proposes a 2D point cloud registration approach based on unsigned distance maps, precomputing the Euclidean distance to the nearest reference point, along with its spatial derivatives, over a discrete grid, replacing the per-iteration search with O(1) lookups. Moreover, point-to-point and point-to-plane error formulations are derived on the SE(2) manifold and solved via Gauss-Newton optimization. On a synthetic benchmark and the real-world IILABS 3D dataset, the precomputed point-to-point variant outperforms its analytical counterparts, achieving competitive laser-odometry drift compared to point-to-plane formulations, as the precomputed gradient regularizes correspondences in the presence of sensor noise.