Neural network-based 3D point cloud detection of targets in unstructured environments

Accurate environmental sensing is an important prerequisite for autonomous driving in off-road environments. Most targets in off-road environments do not have regular shapes, colors, textures and other features, making them difficult to identify. In addition, complex driving conditions can cause large, broadband vibrations in off-road vehicles, which interfere with environment sensing and affect the accuracy and efficiency of perception. To address the above problems, this paper proposes an improved 3D point cloud filtering algorithm for unstructured environments and a point cloud classification method using neural networks, and provides an experimental proof-of-principle of the proposed methods. A comparison of the results under six conditions shows that the amount of data processed by the improved filtering algorithm is 65%–85% of that processed by the conventional filtering algorithm, and the trained neural network model achieves an accuracy of 98.0% and a loss value as low as 0.008 when classifying three typical targets in an unstructured environment. A comparison with algorithms proposed in other papers shows that the proposed method is highly feasible.