A Safety-Assured Semantic Map for an Unstructured Terrain Environment towards Autonomous Engineering Vehicles

Abstract

Accurate obstacle detection plays a crucial role in the creation of high-precision maps within unstructured terrain environments, as it supplies vital decision-making information for unmanned engineering vehicles. Existing works primarily focus on the semantic segmentation of terrain environments, overlooking the safety aspect of vehicle driving. This paper presents a hazardous obstacle detection framework in addition to driving safety-assured semantic information in the generated high-precision map of unstructured scenarios. The framework encompasses the following key steps. Firstly, a continuous terrain point cloud model is obtained, and a pre-processing algorithm is designed to filter noise and fill holes in the point cloud dataset. The Sobel-G operator is then utilized to establish a digital gradient model, facilitating the labeling of hazardous obstacles. Secondly, a bidirectional long short-term memory (Bi-LSTM) neural network is trained on obstacle categories. Finally, by considering the geometric driving state of the vehicle, obstacles that pose safety risks to the vehicle are accurately extracted. The proposed algorithm is validated through experiments conducted on existing datasets as well as real, unstructured terrain point clouds reconstructed by drones. The experimental results affirm the accuracy and feasibility of the proposed algorithm for obstacle information extraction in unstructured scenes.

Keywords

• unstructured terrain environments
• hazardous obstacle detection
• driving safety-assured semantic information
• Bi-LSTM
• geometric driving state