In this paper, we present a recognition-based
autonomous navigation system for mobile robots. The system is
based on our previously proposed Robust Invariant Feature
(RIF) detector. This detector extracts highly robust and
repeatable features based on the key idea of tracking multiscale
interest points and selecting unique representative local
structures with the strongest response in both spatial and scale
domains. Weighted Zernike moments are used as the feature
descriptor and applied to the place recognition. The navigation
system is composed of on-line and off-line two stages. In the
off-line learning stage, we train the robot in its workspace by
just taking several images of representative places as
landmarks. Then, in the on-line navigation stage, the robot
recognizes scenes, obtains robust feature correspondences, and
navigates the environment autonomously using the Iterative
Pose Converging (IPC) algorithm which is based on the idea of
the visual servoing technique. The experimental results and the
performance evaluation show that the proposed navigation
system can achieve excellent performance in complex indoor
environments.