HO3-SLAM: Human-Object Occlusion Ordering as Add-on for Enhancing Traversability Prediction in Dynamic SLAM

Detecting traversable regions from monocular vision is a fundamental problem for indoor robot navigation. In existing works, this is typically formulated as the problem of reconstructing floor regions that are directly visible to the robot. However, the assumption of floor visibility is often violated in crowded scenes such as office environments, which make the existing solutions less effective. To complement existing solutions, we propose a novel approach based on pedestrian observation, i.e., observing the behaviour of pedestrians assuming that the floor region they are standing on is traversable. In our observation, it is recently becoming realistic to implement this new approach by combining state-of-the-art of computer vision techniques, including (1) detection of dynamic objects (e.g., YOLO object detector), (2) absolute localization of stationary objects (e.g., SLAM), and (3) relative localization between dynamic and stationary objects (e.g., Occlusion ordering). A prototype system was implemented by extending state-of-the-art techniques of dynamic SLAM and object detector models, and its basic performance was demonstrated through real-world experiments. We believe that our approach can offer a valuable solution for challenging application domains such as human robot collaboration and long-horizon action planning.