Being in Two Places at Once: Smooth Visual Path Following on Globally Inconsistent Pose Graphs

Early work in the field of SLAM asserted that globally metrically consistent maps expressed in a single coordinate frame were necessary for autonomous operation. It has been shown previously that chain-structured and tree-structured optometric maps provide sufficient information for accurate path following. This paper extends this concept to arbitrarily connected graph structures with loop closures. We show that globally inconsistent maps may be treated as a set of locally defined Riemannian manifolds, and that this representation is sufficient for path repetition tasks. We demonstrate smooth path following on an inconsistent optometric map with loop closures, using the existing Visual Teach and Repeat (VT&R) framework for vision-in-the-loop control. Path-tracking errors are maintained within nominal values despite disparities of over 2m between the local and global representations of robot pose. Traversal of large map discontinuities is found to have no adverse effect on robot performance, allowing segments of the map to be repeated in a different order than they were trained.