Semantic Scene Completion Based 3D Traversability Estimation for Off-Road Terrains
Journal:
arXiv
Published Date:
Dec 11, 2024
Abstract
Off-road environments present significant challenges for autonomous ground
vehicles due to the absence of structured roads and the presence of complex
obstacles, such as uneven terrain, vegetation, and occlusions. Traditional
perception algorithms, designed primarily for structured environments, often
fail under these conditions, leading to inaccurate traversability estimations.
In this paper, ORDformer, a novel multimodal method that combines LiDAR point
clouds with monocular images, is proposed to generate dense traversable
occupancy predictions from a forward-facing perspective. By integrating
multimodal data, environmental feature extraction is enhanced, which is crucial
for accurate occupancy estimation in complex terrains. Furthermore, RELLIS-OCC,
a dataset with 3D traversable occupancy annotations, is introduced,
incorporating geometric features such as step height, slope, and unevenness.
Through a comprehensive analysis of vehicle obstacle-crossing conditions and
the incorporation of vehicle body structure constraints, four traversability
cost labels are generated: lethal, medium-cost, low-cost, and free.
Experimental results demonstrate that ORDformer outperforms existing approaches
in 3D traversable area recognition, particularly in off-road environments with
irregular geometries and partial occlusions. Specifically, ORDformer achieves
over a 20\% improvement in scene completion IoU compared to other models. The
proposed framework is scalable and adaptable to various vehicle platforms,
allowing for adjustments to occupancy grid parameters and the integration of
advanced dynamic models for traversability cost estimation.
