Abstract
Place recognition based on point cloud (LiDAR) scans is an important module for achieving robust autonomy in robots or self-driving vehicles. Training deep networks to match such scans presents a difficult trade-off: a higher spatial resolution of the network's intermediate representations is needed to perform fine-grained matching of subtle geometric features, but growing it too large makes the memory requirements infeasible. In this work, we propose a Point-Voxel Transformer network (PVT3D) that achieves robust fine-grained matching with low memory requirements. It leverages a sparse voxel branch to extract and aggregate information at a lower resolution and a point-wise branch to obtain fine-grained local information. A novel hierarchical cross-attention transformer (HCAT) uses queries from one branch to try to match structures in the other branch, ensuring that both extract self-contained descriptors of the point cloud (rather than one branch dominating), but using both to inform the output global descriptor of the point cloud. Extensive experiments show that the proposed PVT3D method surpasses the state-of-the-art by a large amount on several datasets (Oxford RobotCar, TUM, USyd). For instance, we achieve AR@1 of 85.6% on the TUM dataset, which surpasses the strongest prior model by ~15%.
Abstract (translated)
URL
https://arxiv.org/abs/2211.12542
