Spotting-based global positioning with nonmonotonic continuous DP for mobile robots using image sequences

We develop a technique for robustly positioning and guiding mobile robots in the real world, such as offices and homes, by using topological maps consisting of image sequences capturing the environment. This technique uses reference interval-free continuous dynamic programming (DP) which allows positioning by spotting, but it restricts the direction of mobile robot navigation to the same one as the map creation. We propose to use the nonmonotonic continuous DP for gesture recognition, to allow the positioning of a robot within the same framework even when it navigates backwards or stops. We also propose the use of average values of local features in each section generated by horizontally dividing the omnidirectional view as rotation-invariant features to allow global positioning of a robot regardless of its direction. In addition, we demonstrate the usefulness of this technique through evaluation experiments using mobile robots.