Global Point Cloud Descriptor for Place Recognition in Indoor Environments

Authors:

• Jacek Komorowski,
• Grzegorz Kurzejamski,
• Monika Wysoczańska,
• Tomasz Trzciński

Abstract

This paper presents an approach for learning-based discriminative 3D point cloud descriptor from RGB-D images for place recognition purposes in indoor environments. Existing methods, such as such as PointNetVLAD, PCAN or LPD-Net, are aimed at outdoor environments and operate on 3D point clouds from LiDAR. They are based on PointNet architecture and designed to process only the scene geometry and do not consider appearance (RGB component). In this paper we present a place recognition method based on sparse volumetric representation and processing scene appearance in addition to the geometry. We also investigate if using two modalities, appearance (RGB data) and geometry (3D structure), improves discriminativity of a resultant global descriptor.