Our paper on self-supervised traversability estimation is accepted to ICRA 2024
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작성자 최고관리자 댓글 조회 작성일 24-03-26 14:14본문
Follow the Footprints: Self-supervised Traversability Estimation for Off-road Vehicle Navigation based on Geometric and Visual Cues
Yurim Jeon (227202), E-In Son (361215), Seung-Woo Seo (205194)
bstract— In this study, we address the off-road traversabil ity 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 develop ing 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 condi tions, 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
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