  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.
