提出基于多光谱与激光雷达的可通行性分析方法，让农业机器人安全穿越作物等柔性障碍物。
Collision-Aware Traversability Analysis for Autonomous Vehicles in the Context of Agricultural Robotics
- 融合激光雷达与多光谱相机构建3D光谱地图，识别可通行的柔性障碍物。
- 引入物理驱动的可通行性度量，考虑机器人重量与尺寸，确保安全通过。
- 适用于复杂农田环境，适合农业机器人自主导航研究者参考。
本文提出一种新型安全导航方法，用于农业机器人在非结构化环境中的自主运行。随着全球环境挑战加剧，机器人技术为减少化学投入、满足日益增长的粮食生产需求提供了有力解决方案。然而，机器人在未结构化农田环境中实现自主与鲁棒运行仍面临重大挑战。作物和高草等可变形障碍物需被识别为可通行区域，而非刚性障碍物。为此，我们提出一种基于3D光谱地图的可通行性分析方法，该地图由激光雷达与多光谱相机重建。该方法使机器人能够区分安全与危险碰撞。我们系统评估了多光谱植被检测指标，并将其融入增强型环境地图。利用此地图，计算出考虑机器人重量与尺寸的物理驱动可通行性度量，确保在可变形障碍物上安全行进。
原文摘要 · Abstract (English)
In this paper, we introduce a novel method for safe navigation in agricultural robotics. As global environmental challenges intensify, robotics offers a powerful solution to reduce chemical usage while meeting the increasing demands for food production. However, significant challenges remain in ensuring the autonomy and resilience of robots operating in unstructured agricultural environments. Obstacles such as crops and tall grass, which are deformable, must be identified as safely traversable, compared to rigid obstacles. To address this, we propose a new traversability analysis method based on a 3D spectral map reconstructed using a LIDAR and a multispectral camera. This approach enables the robot to distinguish between safe and unsafe collisions with deformable obstacles. We perform a comprehensive evaluation of multispectral metrics for vegetation detection and incorporate these metrics into an augmented environmental map. Utilizing this map, we compute a physics-based traversability metric that accounts for the robot's weight and size, ensuring safe navigation over deformable obstacles.
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