We present a novel algorithm for learning-based loop-closure for SLAM (simultaneous localization and mapping) applications. Our approach is designed for general 3D point cloud data, including those from lidar, and is used to prevent accumulated drift over time for autonomous driving. We voxelize the point clouds into coarse voxels and calculate the overlap to estimate if the vehicle drives in a loop. We perform point-level registration to compute the current pose accurately. We have evaluated our approach on well-known datasets KITTI, KITTI-360, Nuscenes, Complex Urban, NCLT, and MulRan. We show at most 2 times improvement in accuracy estimation of translation and rotation. On some challenging sequences, our method is the first approach that can obtain a 100% success rate.

翻译：我们提出了一种基于学习的SLAM（同时定位与地图构建）闭环检测新算法。该方法面向通用三维点云数据（包括激光雷达数据），用于消除自动驾驶中随时间累积的定位漂移。通过将点云体素化为粗粒度体素并计算重叠度，我们能够判断车辆是否处于闭环行驶状态。我们进一步执行点级配准以精确计算当前位姿。在KITTI、KITTI-360、Nuscenes、Complex Urban、NCLT及MulRan等公开数据集上的评估表明：本方法在平移和旋转精度估计上实现了最高2倍的提升。对于某些具有挑战性的序列，本文方法首次实现了100%的成功率。