Qualitative localization using vision and odometry for path following in topo-metric maps

We address the problem of navigation in topo- metric maps created by using odometry data and visual loop- closure detection. Based on our previous work [6], we present an optimized version of our loop-closure detection algorithm that makes it possible to create consistent topo-metric maps in real-time while the robot is teleoperated. Using such a map, the proposed navigation algorithm performs qualitative localization using the same loop-closure detection framework and the odometry data. This qualitative position is used to support robot guidance to follow a predicted path in the topo-metric map compensating the odometry drift. Compared to purely visual servoing approaches for similar tasks, our path-following algorithm is real-time, light (not more than two images per seconds are processed), and robust as odometry is still available to navigate even if vision information is absent for a short time. The approach has been validated experimentally with a Pioneer P3DX robot in indoor environments with embedded and remote computations.