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The integration of a SLAM algorithm with place recognition technologyempowers it with the ability to mitigate accumulated errors and to relocalizeitself. However, existing methods for point cloud-based place recognitionpredominantly rely on the matching of descriptors, which are mostlylidar-centric. These methods suffer from two major drawbacks: first, theycannot perform place recognition when the distance between two point clouds issignificant, and second, they can only calculate the rotation angle withoutconsidering the offset in the X and Y directions. To overcome theselimitations, we propose a novel local descriptor that is constructed around theMain Object. By using a geometric method, we can accurately calculate therelative pose. We have provided a theoretical analysis to demonstrate that thismethod can overcome the aforementioned limitations. Furthermore, we conductedextensive experiments on KITTI Odometry and KITTI360, which indicate that ourproposed method has significant advantages over state-of-the-art methods.

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