Dynamic human-object interaction detection for feature exclusion in visual simultaneous localization and mapping (SLAM)

Visual simultaneous localization and mapping (SLAM) remains a focal point in robotics research, particularly in the realm of mobile robots. Despite the existence of robust methods such as ORBSLAM3, their effectiveness is limited in dynamic scenarios. The influence of moving entities in these scenarios poses challenges to data association, leading to compromised pose estimation accuracy. This paper proposes a novel approach that utilizes spatial reasoning to reduce the influence of dynamic entities present in an environment. Our approach, known as human-object interaction detection, identifies the dynamic nature of an object by evaluating the intersecting area between the bounding boxes of a person and the object. We tested our approach by extending the ORBSLAM3 RGB-D SLAM algorithm. Consequently, all ORB features associated with dynamic objects are filtered out from the ORBSLAM3 tracking thread. To validate our approach, we conducted evaluations on highly dynamic sequences extracted from the TUM RGB-D dataset. Our results exhibited a significant performance enhancement over ORBSLAM3. Furthermore, in comparison to other state-of-the-art research, our results remained competitive, given the simplicity of our approach.