LUOJIA Explorer PMS: Panoramic Odometry and Mapping with Structural Information

Abstract. Accurate dense reconstruction of unknown spatial environments is crucial for applications such as underground exploration and planetary missions. Existing methods face challenges like observing blind spots and the difficulty of edge feature extraction in point clouds with non-repetitive scanning LiDARs. This paper first uses a novel odometry and mapping system integrating two solid-state LiDARs and an IMU to obtain distortion-compensated point clouds and corresponding poses, which are utilized to generate submaps. Our approach then leverages these accumulated submaps to efficiently extract edge features. Experimental results demonstrate that our submap-based method effectively identifies edge features within point clouds, which can be used for association with panoramas for joint optimization in the future.