Abstract
Navigating densely vegetated environments poses significant challenges for autonomous ground vehicles. Learning-based systems typically use prior and in-situ data to predict terrain traversability but often degrade in performance when encountering out-of-distribution elements caused by rapid environmental changes or novel conditions. This paper presents a novel, lidar-only, online adaptive traversability estimation (TE) method that trains a model directly on the robot using self-supervised data collected through robot-environment interaction. The proposed approach utilises a probabilistic 3D voxel representation to integrate lidar measurements and robot experience, creating a salient environmental model. To ensure computational efficiency, a sparse graph-based representation is employed to update temporarily evolving voxel distributions. Extensive experiments with an unmanned ground vehicle in natural terrain demonstrate that the system adapts to complex environments with as little as 8 minutes of operational data, achieving a Matthews Correlation Coefficient (MCC) score of 0.63 and enabling safe navigation in densely vegetated environments. This work examines different training strategies for voxel-based TE methods and offers recommendations for training strategies to improve adaptability. The proposed method is validated on a robotic platform with limited computational resources (25W GPU), achieving accuracy comparable to offline-trained models while maintaining reliable performance across varied environments.
Abstract (translated)
在植被密集的环境中导航对自主地面车辆构成了重大挑战。基于学习的方法通常使用先验和现场数据来预测地形可通行性，但在遇到由快速环境变化或新情况引起的分布外元素时，性能往往会下降。本文提出了一种新颖、仅使用激光雷达（LiDAR）的在线自适应可通行性估计（TE）方法，该方法直接在机器人上训练模型，通过机器人与环境之间的交互收集自我监督数据。所提出的方案利用概率3D体素表示法来整合激光雷达测量值和机器人的经验，创建一个显著的环境模型。为了确保计算效率，采用了一种稀疏图基表示法来更新暂时演化的体素分布。使用无人驾驶地面车辆在自然地形中进行广泛的实验表明，该系统只需8分钟的操作数据即可适应复杂环境，并实现了0.63的马修斯相关系数（MCC）评分，在密集植被环境中实现了安全导航。这项工作研究了基于体素的TE方法的不同训练策略，并为提高适应性的培训策略提供了建议。所提出的方法在计算资源有限（25W GPU）的机器人平台上得到了验证，其准确性与离线训练模型相当，同时在各种环境下保持可靠的性能。
URL
https://arxiv.org/abs/2502.01987
