Hybrid vision-based navigation for mobile robots in mixed indoor/outdoor environments

• We propose a new hybrid method for vision-based autonomous navigation.
• It can be used for any kind of mobile robot equipped with a single, consumer camera.
• It can be deal both with mixed indoor/outdoor environments.
• A theoritical convergence analysis is presented.
• Experimental results prove the accuracy and repeatability properties of the method.

In this paper we present a vision-based navigation system for mobile robots equipped with a single, off-the-shelf camera in mixed indoor/outdoor environments. A hybrid approach is proposed, based on the teach-and-replay technique, which combines a path-following and a feature-based navigation algorithm. We describe the navigation algorithms and show that both of them correct the robot’s lateral displacement from the intended path. After that, we claim that even though neither of the methods explicitly estimates the robot position, the heading corrections themselves keep the robot position error bound. We show that combination of the methods outperforms the pure feature-based approach in terms of localization precision and that this combination reduces map size and simplifies the learning phase. Experiments in mixed indoor/outdoor environments were carried out with a wheeled and a tracked mobile robots in order to demonstrate the validity and the benefits of the hybrid approach.