Robust long-range teach-and-repeat in non-urban environments

Abstract

We present a robust teach-and-repeat system enabling an autonomous car to carry out transportation tasks in non-urban environments. Our vehicle first learns a desired route between two arbitrary places by following a human guide tracked by a marker-less, LiDAR-based object tracking algorithm. By moving backward along the route, the guide has the ability to make the vehicle correct the recorded path while it is still navigating autonomously. In the second phase, the car starts shuttling between the route's end points. To this end, a Hybrid A*-based planner generates dynamically feasible, collision-free trajectories whose costs are based on a multitude of features of the local environment, such as slopes, vegetation and road probabilities. A hierarchical state machine coordinates the interaction between the various software modules and monitors the mission's progress. When it detects a road blockage during shuttling, it uses Open Street Map data to find an alternative route to the current goal. We tested the system extensively on our autonomous car MuCAR-3. Apart from data recorded during test runs, we also present the results of our participation in the European Land Robot Trial (ELROB) 2016 competition, where MuCAR-3 took first place in the MULE scenario.