Lo-SLAM: Lunar Target-Oriented SLAM Using Object Identification, Relative Navigation, and Multilevel Mapping

To ensure long-term space missions, an autonomous localization and mapping system for lunar rovers is demanded. While the target-oriented localization and mapping problem can be solved through state-of-the-art methods, they greatly rely on human-in-loop remote operations, posing several challenges for the visual system of a rover when operating in a distant, unknown, and feature-sparse lunar environment. This article presents a segment anything model (SAM)-augmented target-oriented simultaneous localization and mapping (SLAM) framework that enables rovers to estimate the relative distance to the target on the lunar surface, thus ensuring the safety of the exploration task. Based on the proposed point-prompted object instance extraction (OIE) pipeline, object correspondences are first predicted in the middle-end of Lo-SLAM, where reliable semantic constraints are robustly associated cross image frames. We then maintain camera-object relative positioning between the camera and target in the visual odometry of Lo-SLAM. Meanwhile, a multilevel mapping and representation framework is proposed to keep the target explicit and characterized in different subtasks. Extensive experiments are conducted on our dataset, stereo planetary tracks (SePTs). Results show that the proposed Lo-SLAM is validated on challenging lunar scenarios with dramatic viewpoints and object scale changes. The average pose errors are 0.36 m in centroid and 0.27 m in scale, and the average object-centric trajectory error is 0.49% or so. An open-source dataset has been released at https://github.com/miaTian99/SePT_Stereo-Planetary-Tracks.