MC2PS: Cloud-Based 3-D Place Recognition Using Map Segmentation Coordinates Points

Place recognition, which allows distinguishing one location from another, is an extremely challenging problem. Current approaches have limitations such as susceptibility to large environmental changes, learning requirements, and recognition latency. This letter presents a cloud-based place recognition system that does not require any form of learning. The proposed method refines the cloud database by segmentation of 3-D maps to a set of sub-maps and distinguishes them according to their highest z-coordinate points as an efficient search algorithm. In addition, with the presented technique, implementation depends on a parallel computation architecture to speed up the complex stages of the place recognition. In order to evaluate the presented approach, the experiments are carried out with publicly available 3-D scan data sets. The results are compared to the state-of-the-art techniques, which prove that the computational cost is greatly decreased regardless of the size of the compared maps.