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A Multi-Environment Freespace Detection Method Based on Range Scale Map
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AI
DOI：10.1109/TITS.2025.3568165.png)
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Abstract
En 中文
Accurately freespace detection is crucial to ensure the safe operation of autonomous vehicles. However, creating multi-scene datasets can be challenging. Mainstream research primarily addresses driving scenes in urban settings while neglecting other types of road environments. This results in a constrained application environment for current freespace detection methods. This paper proposes an adaptive environment scale freespace detection method in 2D image space. The method does not require data labeling and has better environmental adaptability. The core idea is to adaptively map a fixed point cloud scale in 3D space to a pixel scale in 2D space using the camera projection relation to obtain the fine environmental gradient. Then design search rule to label freespace in 2D space. Experiments on two public datasets, urban and field, achieved F1 scores of 92.50% and 89.09%, respectively. In both structured and unstructured environments, the proposed method demonstrated higher accuracy and lower false detection rates compared to state-of-the-art methods.
Keywords:
Freespace
range scale map
multi-environment
point cloud
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