A Novel Real-Time Edge-Guided LiDAR Semantic Segmentation Network for Unstructured Environments

Abstract

LiDAR-based semantic segmentation, particularly for unstructured environments, plays a crucial role in environment perception and driving decisions for unmanned ground vehicles. Unfortunately, chaotic unstructured environments, especially the high-proportion drivable areas and large-area static obstacles therein, inevitably suffer from the problem of blurred class edges. Existing published works are prone to inaccurate edge segmentation and have difficulties dealing with the above challenge. To this end, this paper proposes a real-time edge-guided LiDAR semantic segmentation network for unstructured environments. First, the main branch is a lightweight architecture that extracts multi-level point cloud semantic features; Second, the edge segmentation module is designed to extract high-resolution edge features using cascaded edge attention blocks, and the accuracy of extracted edge features and the consistency between predicted edge and semantic segmentation results are ensured by additional supervision; Third, the edge guided fusion module fuses edge features and main branch features in a multi-scale manner and recalibrates the channel feature using channel attention, realizing the edge guidance to semantic segmentation and further improving the segmentation accuracy and adaptability of the model. Experimental results on the SemanticKITTI dataset, the Rellis-3D dataset, and on our test dataset demonstrate the effectiveness and real-time performance of the proposed network in different unstructured environments. Especially, the network has state-of-the-art performance in segmentation of drivable areas and large-area static obstacles in unstructured environments.