Navigation without localisation: reliable teach and repeat based on the convergence theorem
This addresses the challenge of robust and efficient navigation for mobile robots in dynamic environments, offering a novel approach that simplifies traditional methods.
The paper tackles the problem of teach-and-repeat visual navigation for mobile robots by proposing a method that eliminates the need for explicit localization, instead using camera information only to correct heading while replaying learned velocities. The result is a computationally efficient, calibration-free system that reliably guides robots in varied conditions, as demonstrated in indoor and outdoor experiments.
We present a novel concept for teach-and-repeat visual navigation. The proposed concept is based on a mathematical model, which indicates that in teach-and-repeat navigation scenarios, mobile robots do not need to perform explicit localisation. Rather than that, a mobile robot which repeats a previously taught path can simply `replay' the learned velocities, while using its camera information only to correct its heading relative to the intended path. To support our claim, we establish a position error model of a robot, which traverses a taught path by only correcting its heading. Then, we outline a mathematical proof which shows that this position error does not diverge over time. Based on the insights from the model, we present a simple monocular teach-and-repeat navigation method. The method is computationally efficient, it does not require camera calibration, and it can learn and autonomously traverse arbitrarily-shaped paths. In a series of experiments, we demonstrate that the method can reliably guide mobile robots in realistic indoor and outdoor conditions, and can cope with imperfect odometry, landmark deficiency, illumination variations and naturally-occurring environment changes. Furthermore, we provide the navigation system and the datasets gathered at http://www.github.com/gestom/stroll_bearnav.