  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.
