We present a novel algorithm specially designed for loop detection and registration that utilizes Lidar-based perception. Our approach to loop detection involves voxelizing point clouds, followed by an overlap calculation to confirm whether a vehicle has completed a loop. We further enhance the current pose's accuracy via an innovative point-level registration model. The efficacy of our algorithm has been assessed across a range of well-known datasets, including KITTI, KITTI-360, Nuscenes, Complex Urban, NCLT, and MulRan. In comparative terms, our method exhibits up to a twofold increase in the precision of both translation and rotation estimations. Particularly noteworthy is our method's performance on challenging sequences where it outperforms others, being the first to achieve a perfect 100% success rate in loop detection.

翻译：我们提出一种专为基于激光雷达感知的闭环检测与配准任务设计的新型算法。在闭环检测环节，我们采用点云体素化处理，并通过重叠度计算确认车辆是否完成闭环遍历。通过创新的点级配准模型，我们进一步提升了当前位姿的估计精度。该算法在KITTI、KITTI-360、Nuscenes、Complex Urban、NCLT及MulRan等多个公开数据集上进行了性能评估。实验结果表明，与现有方法相比，本方法在平移估计与旋转估计精度上均实现最高两倍的提升。尤为值得关注的是，在具有挑战性的序列中，本方法首次以100%的完美成功率完成闭环检测，显著超越其他对比方法。