HSS-SLAM: Human-in-the-Loop Semantic SLAM Represented by Superquadrics

The advancement of object detection algorithms has catalyzed the development of object-level semantic SLAM. However, due to missed and false detections, object-level se- mantic SLAM fails to represent the objects within the scene adequately. Therefore, this paper proposes a novel object-level semantic SLAM termed HSS-SLAM. We incorporate human- in-the-loop into our method, establishing an interaction module to facilitate human editing and rectifying semantic information. Additionally, to minimize the manual correction workload, a lightweight and intuitive method for semantic extension is proposed, augmenting the semantic richness of the global map with a few operations. Furthermore, our method adopts superquadrics for object representation, enabling detailed de- scriptions of various object shapes. This mitigates the limitation of conventional semantic mapping, where objects are difficult to distinguish due to the reliance on a single-shape representation. Subsequently, precise estimation of superquadric parameters and camera poses is achieved through joint optimization. Extensive experiments conducted on TUM RGB-D and Scenes V2 datasets demonstrate that the proposed approach exhibits competitive performance, surpassing current methods in both object representation and camera localization accuracy.