Semantic Loop Closure Detection for Intelligent Vehicles Using Panoramas

Loop Closure Detection is of great significance in the field of intelligent driving systems, as it reduces the cumulative error of the estimated position of the system and assists in generating a consistent global map. Existing methods differ in frame representation methods and the corresponding frame-matching strategy. Traditionally, local feature points and descriptors are studied extensively while recently global descriptors and semantic information extracted from deep learning methods are considered superior in terms of promoting a high-level understanding of the surrounding environments of robots. However, one of the most challenging problems of using semantic information for loop detection is how to deal with inconsistent visual contents from different viewpoints in the same place. In this article, a semantic loop closure detection method using panoramas is proposed to address this issue. We design a pipeline for efficiently extracting and matching semantic information between frames to identify loops. Most importantly we propose a novel polar coordinate-based panorama representation to address the inconsistent visual appearance problem caused by viewpoint differences. Experiment results show that our proposed method can significantly increase the accuracy of loop closure detection tasks in challenging scenarios where traditional methods may fail.