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 difculty 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 coarseto-
ne 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.