VertiAdaptor: Online Kinodynamics Adaptation for Vertically Challenging Terrain
Tong Xu, Chenhui Pan, Aniket Datar, Xuesu Xiao

TL;DR
VertiAdaptor introduces an online adaptation framework that enhances kinodynamic modeling for autonomous vehicles navigating unpredictable off-road terrains by integrating elevation and semantic data, leading to improved accuracy and faster adaptation.
Contribution
This work presents a novel online adaptation method using neural ODE basis functions for real-time terrain-aware kinodynamic modeling in off-road autonomous driving.
Findings
Improves prediction accuracy by up to 23.9%.
Achieves 5X faster adaptation time.
Validated in simulation and on physical platform.
Abstract
Autonomous driving in off-road environments presents significant challenges due to the dynamic and unpredictable nature of unstructured terrain. Traditional kinodynamic models often struggle to generalize across diverse geometric and semantic terrain types, underscoring the need for real-time adaptation to ensure safe and reliable navigation. We propose VertiAdaptor (VA), a novel online adaptation framework that efficiently integrates elevation with semantic embeddings to enable terrain-aware kinodynamic modeling and planning via function encoders. VA learns a kinodynamic space spanned by a set of neural ordinary differential equation basis functions, capturing complex vehicle-terrain interactions across varied environments. After offline training, the proposed approach can rapidly adapt to new, unseen environments by identifying kinodynamics in the learned space through a…
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Taxonomy
TopicsAutonomous Vehicle Technology and Safety · Robotic Path Planning Algorithms · Robotics and Sensor-Based Localization
