Producing traversability maps and understanding the surroundings are crucial prerequisites for autonomous navigation. In this paper, we address the problem of traversability assessment using point clouds. We propose a novel pillar feature extraction module that utilizes PointNet to capture features from point clouds organized in vertical volume and a 2D encoder-decoder structure to conduct traversability classification instead of the widely used 3D convolutions. This results in less computational cost while even better performance is achieved at the same time. We then propose a new spatio-temporal attention module to fuse multi-frame information, which can properly handle the varying density problem of LIDAR point clouds, and this makes our module able to assess distant areas more accurately. Comprehensive experimental results on augmented Semantic KITTI and RELLIS-3D datasets show that our method is able to achieve superior performance over existing approaches both quantitatively and quantitatively.

翻译：生成可通行性地图并理解周围环境是自主导航的关键前提。本文针对使用点云进行可通行性评估的问题展开研究。我们提出了一种新颖的柱体特征提取模块，该模块利用PointNet从垂直体素化组织的点云中捕获特征，并采用二维编码器-解码器结构进行可通行性分类，以替代广泛使用的三维卷积操作。该方法在实现更优性能的同时显著降低了计算成本。我们进一步提出了一种新的时空注意力模块来融合多帧信息，该模块能够有效处理激光雷达点云密度分布不均的问题，从而提升对远距离区域评估的准确性。在增强版Semantic KITTI和RELLIS-3D数据集上的综合实验结果表明，本方法在定量与定性评估上均优于现有方法。