SMORE-SLAM: Semantic Monocular SLAM with Scale Correction and Reverse Loop Utilization in Outdoor Environments

In large-scale outdoor environments, vehicles often encounter situations like retracing their path or turning around, leading to many reverse loop closures where the vehicles traverse previously covered paths from opposite viewpoints. Existing monocular SLAM methods, due to insufficient utilization of semantic information and neglect of leveraging reverse loop closures, result in significant scale drift and pose drift when confronted with such scenarios. In this paper, we introduce SMORE-SLAM, a semantic monocular SLAM with scale correction and reverse loop closure module. We constrain scale drift by harnessing semantic information across a wide spatial extent. Furthermore, we detect and correct reverse loop closures using semantic point cloud to reduce pose drift. Experimental results on the KITTI odometry dataset and the Oxford RobotCar dataset demonstrate the capability of our research in scale correction and reverse loop closure detection, enabling a reduction in trajectory errors of monocular SLAM.