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This paper presents a fully unsupervised deep change detection approach formobile robots with 3D LiDAR. In unstructured environments, it is infeasible todefine a closed set of semantic classes. Instead, semantic segmentation isreformulated as binary change detection. We develop a neural network,RangeNetCD, that uses an existing point-cloud map and a live LiDAR scan todetect scene changes with respect to the map. Using a novel loss function,existing point-cloud semantic segmentation networks can be trained to performchange detection without any labels or assumptions about local semantics. Wedemonstrate 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 thegeometric baseline used in all experiments. The neural network runs faster than10Hz and is integrated into a robot's autonomy stack to allow safe navigationaround obstacles that intersect the planned path. In addition, a novel methodfor the rapid automated acquisition of per-point ground-truth labels isdescribed. Covering changed parts of the scene with retroreflective materialsand applying a threshold filter to the intensity channel of the LiDAR allowsfor quantitative evaluation of the change detector.

Change of Scenery: Unsupervised LiDAR Change Detection for Mobile Robots