Abstract-High-resolution 3D scanning can improve the performance of object detection and door opening, two tasks critical to the operation of mobile manipulators in cluttered homes and workplaces. We discuss how high-resolution depth information can be combined with visual imagery to improve the performance of object detection beyond what is (currently) achievable with 2D images alone, and we present door-opening and inventory-taking experiments.
I. INTRODUCTIONIn this paper, we propose employing high-resolution 3D sensing on mobile manipulators. Just as the change from sonar-based sensing to laser-based sensing enabled drastic improvement of SLAM in mobile robotics, we propose that dramatically improving the quality of depth estimation on mobile manipulators can enable new classes of algorithms and higher levels of performance (Figure 1). In support of this idea, we present two scenarios where high-accuracy 3D data proves useful to large mobile manipulators operating in cluttered environments.The first scenario involves object detection. In many tasks, a mobile manipulator needs to search for an object class in a cluttered environment. This problem is challenging when only visual information is given to the system: variations in background, lighting, scene structure, and object orientation exacerbate an already-difficult problem. We demonstrate that augmenting state-of-the-art computer vision techniques with high-resolution 3D information results in higher precision and recall than is currently achievable by either modality alone.The second scenario involves manipulator trajectory planning. We demonstrate closed-loop perception and manipulation of door handles using information from both visual images and the 3D scanner. The high-resolution 3D information helps ensure that the trajectory planner keeps the manipulator clear of the door while still contacting the door handle.We then present an application experiment which combines these capabilities to perform a simple inventory-control task. The mobile manipulator enters several offices and searches for an object class, recording the detected locations.
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