HD Map Change Detection with Cross-Domain Deep Metric Learning

High-definition (HD) maps are emerging as an essential tool for autonomous driving since they provide high-precision semantic information about the physical environment. To function as a reliable source of map information, HD maps must be constantly updated with changes that occur to the state of the road. In this paper, we propose a novel framework for HD map change detection that can be used to maintain an up-to-date HD map. More specifically, we design our HD map change detection algorithm based on deep metric learning, providing a unified framework that directly maps an input image to estimated probabilities of HD map changes. To reduce the discrepancy between input domains, i.e., camera image and HD map, we propose an effective learning scheme for metric space based on adversarial learning. Finally, we augment our framework with a pixel-level local change detector that specifies the region of changes in the image. We verify the effectiveness of our framework by evaluating it on a city-scale urban HD map dataset. Experimental results show that our method can robustly detect changes against noises due to dynamic objects and error in vehicle poses.