One of the key challenges in high-speed off-road navigation on ground vehicles is that the kinodynamics of the vehicle-terrain interaction can differ dramatically depending on the terrain. Previous approaches to addressing this challenge have considered learning an inverse kinodynamics (IKD) model, conditioned on inertial information of the vehicle to sense the kinodynamic interactions. In this paper, we hypothesize that to enable accurate high-speed off-road navigation using a learned IKD model, in addition to inertial information from the past, one must also anticipate the kinodynamic interactions of the vehicle with the terrain in the future. To this end, we introduce Visual-Inertial Inverse Kinodynamics (VI-IKD), a novel learning-based IKD model that is conditioned on visual information from a terrain patch ahead of the robot in addition to past inertial information, enabling it to anticipate kinodynamic interactions in the future. We validate the effectiveness of VI-IKD in accurate high-speed off-road navigation experimentally on a scale 1/5 UT-AlphaTruck off-road autonomous vehicle in both indoor and outdoor environments and show that compared to other state-of-the-art approaches, VI-IKD enables more accurate and robust off-road navigation on a variety of different terrains at speeds of up to 3.5m/s.

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In International Conference on Intelligent Robots and Systems, 2022, Kyoto, Japan, October 2022.

@inproceedings{IROS22-karnan, title={VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics}, author={Haresh Karnan and Kavan Sikand and Pranav Atreya and Sadegh Rabiee and Xuesu Xiao and Garrett Warnell and Peter Stone and Joydeep Biswas}, booktitle={International Conference on Intelligent Robots and Systems, 2022}, month={October}, address={Kyoto, Japan}, url="http://www.cs.utexas.edu/users/ai-lab?IROS22-karnan", year={2022} }