Robot In a Room: Toward Perfect Object Recognition in Closed Environments
This work addresses the challenge of practical robot vision for end-users in constrained settings like homes or offices, though it is incremental as it builds on existing recognition and mapping techniques.
The paper tackles the problem of achieving near-human-level object recognition for robots in closed environments by leveraging prior exposure to objects and background subtraction via 3D mapping, resulting in highly reliable recognition with human-in-the-loop queries for low confidence or new objects.
While general object recognition is still far from being solved, this paper proposes a way for a robot to recognize every object at an almost human-level accuracy. Our key observation is that many robots will stay in a relatively closed environment (e.g. a house or an office). By constraining a robot to stay in a limited territory, we can ensure that the robot has seen most objects before and the speed of introducing a new object is slow. Furthermore, we can build a 3D map of the environment to reliably subtract the background to make recognition easier. We propose extremely robust algorithms to obtain a 3D map and enable humans to collectively annotate objects. During testing time, our algorithm can recognize all objects very reliably, and query humans from crowd sourcing platform if confidence is low or new objects are identified. This paper explains design decisions in building such a system, and constructs a benchmark for extensive evaluation. Experiments suggest that making robot vision appear to be working from an end user's perspective is a reachable goal today, as long as the robot stays in a closed environment. By formulating this task, we hope to lay the foundation of a new direction in vision for robotics. Code and data will be available upon acceptance.