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arXiv · 2206.03062

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

Haodong Yuan·Yudong Zhang·Shengyin Fan·Xue Li·Jian Wang
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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.

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Haodong Yuan, Yudong Zhang, Shengyin Fan, Xue Li, Jian Wang. 2023-11-13. Object Scan Context: Object-centric Spatial Descriptor for Place Recognition within 3D Point Cloud Map. https://arxiv.org/abs/2206.03062

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