用视觉特征实时预测越野驾驶中的动态变化，提升高速自主导航能力。
Dynamics Modeling using Visual Terrain Features for High-Speed Autonomous Off-Road Driving
- 结合视觉基础模型与地形特征，构建可实时更新的动态模型。
- 在数百公里复杂地形数据上验证，显著提升高速越野行驶稳定性。
- 适合需要高鲁棒性的野外自主车辆系统研发者使用。
快速自主穿越非结构化地形对于灾害响应、搜救或行星探索等场景至关重要。当车辆在极限性能下穿越极端地形时，其动力学特性可能突然且剧烈变化，例如高速和不同地形会影响牵引力、轮胎滑移和滚动阻力。为实现此类环境下的有效规划，需具备能准确预判这些条件的动力学模型。本文提出一种混合模型，基于视觉输入预测由地形引发的动力学变化。我们利用预训练的视觉基础模型DINOv2，获取编码细粒度语义信息的丰富特征。为支持规划应用，提出端到端训练的投影距离无关特征编码器，压缩来自VFM的信息，实现实时轻量级环境地图构建。我们在多个地点采集的海量数据（数百公里激进越野驾驶）上验证了该架构，数据来源于DARPA RACER计划。视频展示：https://www.youtube.com/watch?v=dycTXxEosMk
原文摘要 · Abstract (English)
Rapid autonomous traversal of unstructured terrain is essential for scenarios such as disaster response, search and rescue, or planetary exploration. As a vehicle navigates at the limit of its capabilities over extreme terrain, its dynamics can change suddenly and dramatically. For example, high-speed and varying terrain can affect parameters such as traction, tire slip, and rolling resistance. To achieve effective planning in such environments, it is crucial to have a dynamics model that can accurately anticipate these conditions. In this work, we present a hybrid model that predicts the changing dynamics induced by the terrain as a function of visual inputs. We leverage a pre-trained visual foundation model (VFM) DINOv2, which provides rich features that encode fine-grained semantic information. To use this dynamics model for planning, we propose an end-to-end training architecture for a projection distance independent feature encoder that compresses the information from the VFM, enabling the creation of a lightweight map of the environment at runtime. We validate our architecture on an extensive dataset (hundreds of kilometers of aggressive off-road driving) collected across multiple locations as part of the DARPA Robotic Autonomy in Complex Environments with Resiliency (RACER) program. https://www.youtube.com/watch?v=dycTXxEosMk
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