This paper presents a fully unsupervised deep change detection approach for mobile robots with 3D LiDAR. In unstructured environments, it is infeasible to define a closed set of semantic classes. Instead, semantic segmentation is reformulated as binary change detection. We develop a neural network, RangeNetCD, that uses an existing point-cloud map and a live LiDAR scan to detect scene changes with respect to the map. Using a novel loss function, existing point-cloud semantic segmentation networks can be trained to perform change detection without any labels or assumptions about local semantics. We demonstrate the performance of this approach on data from challenging terrains; mean intersection over union (mIoU) scores range between 67.4% and 82.2% depending on the amount of environmental structure. This outperforms the geometric baseline used in all experiments. The neural network runs faster than 10Hz and is integrated into a robot's autonomy stack to allow safe navigation around obstacles that intersect the planned path. In addition, a novel method for the rapid automated acquisition of per-point ground-truth labels is described. Covering changed parts of the scene with retroreflective materials and applying a threshold filter to the intensity channel of the LiDAR allows for quantitative evaluation of the change detector.

翻译：本文提出了一种完全无监督的深度变化检测方法，适用于搭载3D激光雷达的移动机器人。在非结构化环境中，定义封闭的语义类别集合不可行，因此语义分割被重新表述为二元变化检测问题。我们开发了神经网络RangeNetCD，它利用现有点云地图和实时激光雷达扫描来检测相对于地图的场景变化。通过一种新颖的损失函数，现有点云语义分割网络可在无需任何标签或局部语义假设的条件下进行训练，从而实现变化检测。我们在具有挑战性的地形数据上展示了该方法的性能；根据环境结构的不同，平均交并比（mIoU）得分介于67.4%至82.2%之间，在所有实验中均优于几何基线。该神经网络运行速度超过10Hz，并已集成到机器人自主导航栈中，以支持围绕与规划路径相交的障碍物进行安全导航。此外，本文还描述了一种快速自动获取逐点真实标签的新方法：用反光材料覆盖场景中变化的区域，并对激光雷达强度通道施加阈值滤波器，从而实现对变化检测器的定量评估。