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.

该论文提出了一种新的基于学习的运动学逆解模型：Visual-Inertial Inverse Kinodynamics (VI-IKD)，它结合了惯性和视觉信息，可以预测地形与车辆间的运动学相互作用，提高了无人车高速越野导航的准确性和鲁棒性。