Fast Scan Context Matching for Omnidirectional 3D Scan

Abstract

Autonomous robots need to recognize the environment by identifying the scene. Scan context is one of global descriptors, and it encodes the three-dimensional scan data of the scene for the identification in a matrix form. Scan context is in a matrix form that is simple to store, but the matching of scan contexts can require computational effort because the descriptor is orientation-dependent. Because a scan context of an omnidirectional LiDAR scan becomes periodic in azimuth, this paper proposes to compute the scan context matching efficiently incorporating the cross-correlation with fast Fourier transform, and, hence, the method is named fast scan context matching. The effectiveness of the proposed method for computation time, accuracy, and robustness are reported in this paper. It is also shown that the method was also tested as a loop closure detector of a SLAM package as a practical application and that the proposed method outperformed the conventional scan context matching.