VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics

TL;DR

This paper introduces VI-IKD, a novel visual-inertial learning model that anticipates vehicle-terrain interactions for high-speed off-road navigation, significantly improving accuracy and robustness over existing methods.

Contribution

The paper presents VI-IKD, a new learned inverse kinodynamics model that incorporates visual future terrain information to enhance high-speed off-road navigation.

Findings

VI-IKD achieves more accurate navigation than state-of-the-art methods.

The approach enables robust off-road navigation at speeds up to 3.5 m/s.

Validated on a 1/5 scale UT-AlphaTruck in diverse environments.

Abstract

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 3.5 m/s.