A framework for infrastructure-free warehouse navigation

This paper presents a universally applicable graph-based framework for the navigation of warehouse robots equipped with only monocular cameras. We strongly advocate the use of relative pose information stored in a topological map, rather than a globally consistent metric representation of the environment. We show how multiple traversals of adjacent workspaces can be naturally “stitched” together in the course of a typical warehouse picking and shelving schedule to create a network of reusable paths in which the robot can efficiently localise and plan new routes. This allows us to command the robot to return to any of the previously visited locations not necessarily through the same route that we taught it. Unlike state-of-the-art teach and repeat systems using stereo vision, our approach exploits the strongly planar nature of the data obtained from a downward-facing camera, and creates odometric constraints by tracking the perceived texture of the floor and computing a simple homography. To demonstrate the robustness of our system, we validate our approach on datasets collected over a week-long period within a challenging and representative environment in the form of a warehouse shelving area.