Navigation without localisation: reliable teach and repeat based on the convergence theorem

TL;DR

This paper introduces a teach-and-repeat visual navigation method that relies on a mathematical model showing robots can navigate without explicit localisation by replaying learned velocities and correcting heading, ensuring reliable path following.

Contribution

The paper presents a new mathematical proof and a simple monocular navigation method that does not require camera calibration and can handle complex paths and environmental variations.

Findings

Method reliably guides robots indoors and outdoors

It copes with imperfect odometry and environment changes

No explicit localisation or camera calibration needed

Abstract

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.