In this study, we address the off-road traversability estimation problem, that predicts areas where a robot can navigate in off-road environments. An off-road environment is an unstructured environment comprising a combination of traversable and non-traversable spaces, which presents a challenge for estimating traversability. This study highlights three primary factors that affect a robot's traversability in an off-road environment: surface slope, semantic information, and robot platform. We present two strategies for estimating traversability, using a guide filter network (GFN) and footprint supervision module (FSM). The first strategy involves building a novel GFN using a newly designed guide filter layer. The GFN interprets the surface and semantic information from the input data and integrates them to extract features optimized for traversability estimation. The second strategy involves developing an FSM, which is a self-supervision module that utilizes the path traversed by the robot in pre-driving, also known as a footprint. This enables the prediction of traversability that reflects the characteristics of the robot platform. Based on these two strategies, the proposed method overcomes the limitations of existing methods, which require laborious human supervision and lack scalability. Extensive experiments in diverse conditions, including automobiles and unmanned ground vehicles, herbfields, woodlands, and farmlands, demonstrate that the proposed method is compatible for various robot platforms and adaptable to a range of terrains. Code is available at https://github.com/yurimjeon1892/FtFoot.

翻译：本研究针对越野环境中的可通行性估计问题，即预测机器人可在越野场景中导航的区域。越野环境属于非结构化环境，由可通行与不可通行空间混合构成，给可通行性估计带来挑战。本研究揭示了影响机器人越野可通行性的三个主要因素：地表坡度、语义信息及机器人平台。我们提出了两种基于引导滤波网络（GFN）与足迹监督模块（FSM）的可通行性估计策略。第一种策略是采用新设计的引导滤波层构建新型GFN，通过解析输入数据中的地表与语义信息，并将其融合为适用于可通行性估计的优化特征。第二种策略是开发FSM这一自监督模块，利用机器人预行驶路径（即足迹）实现反映机器人平台特性的可通行性预测。基于上述两种策略，所提方法克服了现有方法需人工标注、缺乏可扩展性的局限。在汽车与无人地面车辆、草本植物区、林区及农田等多种场景下的广泛实验表明，该方法可兼容多种机器人平台并适应不同地形。代码见https://github.com/yurimjeon1892/FtFoot。