Research on ArUco Marker-based Loop Closure Detection Method in Cable Tunnel Environment

Simultaneous localization and mapping (SLAM) technology is one of the core technologies of the crawler robot with integrated inspection and firefighting. Due to the existence of degraded environment in the cable tunnel, it leads to a large cumulative error of the system. The correct loop closure detection(LCD) can well eliminate the cumulative error, which is of great significance to the intelligent inspection of the robot. Aiming at the problems of low recall rate of LCD and inability to construct globally consistent trajectories and maps for crawler inspection robots due to similar and single features in cable tunnel environment, a LCD method based on ArUco markers is proposed. In the image feature extraction, the image feature extraction method is designed according to the environmental characteristics of cable tunnel, which makes the image features rich and differentiated; On the construction of loop back key frame, a loop back key frame cluster library for loop back detection is constructed according to the unique ID information of ArUco, which solves the problem that the correct loop back candidate frame cannot be detected in similar environment; in the research of loop detection algorithm, ArUco mark is introduced for loop verification, which improves the recall rate of loop detection. The experimental results show that the method can accurately achieve LCD in the cable tunnel environment. It improves the autonomous inspection accuracy of the inspection robot under the cable tunnel.