Uncertainty-Aware Point-Cloud Semantic Segmentation for Unstructured Roads

Abstract

Semantic segmentation is one of the fundamental elements for achieving effective and safe autonomous driving. However, due to the irregular boundaries and variable illumination of unstructured roads, applying it in these scenarios is confronted with great challenges. To address these problems, a novel point-cloud semantic segmentation framework for unstructured roads is proposed. It contains three sections: spherical projection, an uncertainty-aware semantic segmentation network, and post-processing. Firstly, point cloud will be projected to the range image which can be processed by the 2D convolution network. Then, the uncertainty-aware semantic segmentation network is constructed. It consists of context-aware attention module and direction attention up-sampling module, which can improve the performance for segmentation of unstructured roads. In addition, a gaussian mixture model is introduced at the end of the network to predict the result with uncertainty, indicating the confidence level of the output. Finally, the segmentation result is refined during the post-processing to help filter the noise points. Experiment data from mine sites was collected to validate the performance for unstructured roads. In addition, the proposed method was also evaluated on the public unstructured dataset RELLIS-3D. The experiments show that the proposed architecture achieved 74.9% mIoU and 40.4% mIoU, which performs better than comparison methods. Additionally, the network is more robust to noisy data by achieving improvements of 4.6%-7.6% under different levels of noise data.