  For autonomous driving, traversability analysis is one of the most basic and
essential tasks. In this paper, we propose a novel LiDAR-based terrain modeling
approach, which could output stable, complete and accurate terrain models and
traversability analysis results. As terrain is an inherent property of the
environment that does not change with different view angles, our approach
adopts a multi-frame information fusion strategy for terrain modeling.
Specifically, a normal distributions transform mapping approach is adopted to
accurately model the terrain by fusing information from consecutive LiDAR
frames. Then the spatial-temporal Bayesian generalized kernel inference and
bilateral filtering are utilized to promote the stability and completeness of
the results while simultaneously retaining the sharp terrain edges. Based on
the terrain modeling results, the traversability of each region is obtained by
performing geometric connectivity analysis between neighboring terrain regions.
Experimental results show that the proposed method could run in real-time and
outperforms state-of-the-art approaches.
