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：我们为SLAM(同时本地化和绘图)应用程序提出了一个基于学习的循环闭合的新算法。我们的方法是为通用的 3D 点云数据设计的,包括来自利达尔的云,并用来防止在一段时间内自动驾驶的累积漂移。我们将点云分解成粗糙的氧化物,并计算出如果车辆在循环中驱动时的重叠值来估计。我们进行了点级注册,以准确计算当前方位。我们评估了我们对众所周知的数据集KITTI、KITTI-360、Nuscenes、复杂城市、NCNCLT和MulRan的处理方法。我们最多能显示翻译和旋转准确性估计的2倍改进。关于一些具有挑战性的序列,我们的方法是能够达到100%成功率的第一种方法。</s>