Object Scan Context: Object-centric Spatial Descriptor for Place Recognition within 3D Point Cloud Map
This work addresses place recognition in 3D point cloud maps for SLAM systems, offering improved accuracy in relative pose estimation, though it appears incremental as it builds on existing descriptor-based approaches.
The paper tackles the problem of point cloud-based place recognition by proposing a novel object-centric descriptor that overcomes limitations of existing lidar-centric methods, achieving significant advantages over state-of-the-art methods on KITTI datasets.
The integration of a SLAM algorithm with place recognition technology empowers it with the ability to mitigate accumulated errors and to relocalize itself. However, existing methods for point cloud-based place recognition predominantly rely on the matching of descriptors, which are mostly lidar-centric. These methods suffer from two major drawbacks: first, they cannot perform place recognition when the distance between two point clouds is significant, and second, they can only calculate the rotation angle without considering the offset in the X and Y directions. To overcome these limitations, we propose a novel local descriptor that is constructed around the Main Object. By using a geometric method, we can accurately calculate the relative pose. We have provided a theoretical analysis to demonstrate that this method can overcome the aforementioned limitations. Furthermore, we conducted extensive experiments on KITTI Odometry and KITTI360, which indicate that our proposed method has significant advantages over state-of-the-art methods.