SpoxelNet: Spherical Voxel-based Deep Place Recognition for 3D Point Clouds of Crowded Indoor Spaces

Abstract

With its essential role in achieving full autonomy of robot navigation, place recognition has been widely studied with various approaches. Recently, numerous point cloud-based methods with deep learning implementation have been proposed with promising results for their application in outdoor environments. However, their performances are not as promising in indoor spaces because of the high level of occlusion caused by structures and moving objects. In this paper, we propose a point cloud-based place recognition method for crowded indoor spaces. The method consists of voxelizing point clouds in spherical coordinates and defining the occupancy of each voxel in ternary values. We also present SpoxelNet, a neural network architecture that encodes input voxels into global descriptor vectors by extracting the structural features in both fine and coarse scales. It also reinforces its performance in occluded places by concatenating feature vectors from multiple directions. Our method is evaluated in various indoor datasets and outperforms existing methods with a large margin.