Object Scan Context: Object-centric Spatial Descriptor for Place Recognition within 3D Point Cloud Map

TL;DR

This paper introduces an object-centric spatial descriptor for place recognition in 3D point clouds, addressing limitations of lidar-centric methods by accurately capturing relative pose and improving recognition across large distances.

Contribution

The paper proposes a novel object-focused descriptor that enhances place recognition by overcoming distance and orientation limitations of existing lidar-centric methods.

Findings

Outperforms state-of-the-art methods on KITTI datasets

Accurately calculates relative pose including X, Y, and rotation

Effective in large-distance place recognition scenarios

Abstract

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.