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. Finally, we use factor graphs to modify the poses with different weights along the trajectory of the vehicle to update and modify the map. We have evaluated our approach on well-known datasets KITTI, KITTI-360, Nuscenes, Complex Urban, NCLT, and MulRan. We show more accurate estimation of translation and rotation. On some challenging sequences, our method is the first approach that can obtain a 100% success rate.

翻译：本文提出了一种新的基于学习的闭环检测算法，用于SLAM（同时定位与地图构建）应用。该方法专为通用三维点云数据（包括激光雷达数据）设计，旨在防止自动驾驶中随时间累积的漂移误差。我们通过将点云体素化为粗体素并计算重叠程度，以估计车辆是否行驶至闭环区域。随后执行点级配准以精确计算当前位姿。最终采用因子图沿车辆轨迹以不同权重修正位姿，从而更新并优化地图。在KITTI、KITTI-360、Nuscenes、Complex Urban、NCLT及MulRan等公开数据集上的评估表明，我们的方法在平移与旋转估计上具有更高的精度。针对部分具有挑战性的序列，本方法首次实现了100%的成功率。