  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.
