SeqSphereVLAD: Sequence Matching Enhanced Orientation-invariant Place Recognition

Abstract

Human beings and animals are capable of recognizing places from a previous journey when viewing them under different environmental conditions (e.g., illuminations and weathers). This paper seeks to provide robots with a human-like place recognition ability using a new point cloud feature learning method. This is a challenging problem due to the difficulty of extracting invariant local descriptors from the same place under various orientation differences and dynamic obstacles. In this paper, we propose a novel lightweight 3D place recognition method, SeqSphereVLAD, which is capable of recognizing places from a previous trajectory regardless of the viewpoint and the temporary observation differences. The major contributions of our method lie in two modules: (1) the spherical convolution feature extraction module, which produces orientation-invariant local place descriptors, and (2) the coarse-to-fine sequence matching module, which ensures both accurate loop-closure detection and real-time performance. Despite the apparent simplicity, our proposed approach outperform the state-of-the-arts for place recognition under datasets that combine orientation and context differences. Compared with the arts, our method can achieve above 95% average recall for the best match with only 18% inference time of PointNet-based place recognition methods.