An Efficient Locally Reactive Controller for Safe Navigation in Visual Teach and Repeat Missions

To achieve successful field autonomy, mobile robots need to freely adapt to changes in their environment. Visual navigation systems such as Visual Teach and Repeat (VT&R) often assume the space around the reference trajectory is free, but if the environment is obstructed path tracking can fail or the robot could collide with a previously unseen obstacle. In this work, we present a locally reactive controller for a VT&R system that allows a robot to navigate safely despite physical changes to the environment. Our controller uses a local elevation map to compute vector representations and outputs twist commands for navigation at 10 Hz. They are combined in a Riemannian Motion Policies (RMP) controller that requires less than 2 ms to run on a CPU. We integrated our controller with a VT&R system onboard an ANYmal C robot and tested it in indoor cluttered spaces and a large-scale underground mine. We demonstrate that our locally reactive controller keeps the robot safe when physical occlusions or loss of visual tracking occur such as when walking close to walls, crossing doorways, or traversing narrow corridors.

Accepted in IEEE Robotics and Automation Letters (RA-L) and International Conference on Robotics and Automation (ICRA) 2022.

Authors: Matías Mattamala, Nived Chebrolu, Maurice Fallon

Pre-print: https://arxiv.org/abs/2201.03938
PDF: https://arxiv.org/pdf/2201.03938.pdf