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.

使用点云进行地形可通过性评估，通过新的柱状特征提取模块和时空注意力模块，实现了更高的性能和更准确的评估结果。