A system for docking robotic wheelchair to partially visible table of unknown pose using human input and robotic wheelchair motion

Abstract

Existing autonomous table docking approaches require large part of table to be visible in order to compute good docking pose for a table with unknown pose. However, in real world settings like cluttered offices and food courts, users often need to initiate docking when the table is only partially visible. This work focuses on the development of a table docking system that enables a wheelchair user to dock to a table with unknown pose, even when the table is partially visible by using human input and the wheelchair motion. An intuitive point-and-click interface is implemented to allow user to initiate docking, by simply clicking on table in the RGB image of the scene. Docking pose is calculated by extracting table edge from the point cloud using the information provided by the user’s click and the streaming RGBD camera images of the scene as the wheelchair moves towards the table. Based on our experiments, accurate table docking can be achieved even when the table is only 20% visible using our approach while the baseline system requires at least 70% of the table to be visible. This makes our system applicable in realistic scenarios. During evaluation with human subjects, all the participants preferred our system over the baseline due to ease of use. Quantitatively, our system more than halved the effort required to initiate autonomous docking as compared to the baseline.