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.

翻译：SLAM算法与地点识别技术的结合赋予其消除累积误差和重定位的能力。然而，现有基于点云的地点识别方法主要依赖描述符匹配，且大多数以激光雷达为中心。这些方法存在两个主要缺陷：首先，当两个点云之间距离较大时无法进行地点识别；其次，仅能计算旋转角度而未考虑X和Y方向的偏移。为克服这些限制，我们提出了一种围绕主物体构建的新型局部描述符。通过几何方法，我们能够精确计算相对位姿。我们提供了理论分析证明该方法可以克服上述局限性。此外，我们在KITTI里程计和KITTI360上进行了广泛实验，结果表明我们提出的方法相较于现有最先进方法具有显著优势。