Real-time Neural Dense Elevation Mapping for Urban Terrain with Uncertainty Estimations
arxiv(2022)
摘要
Having good knowledge of terrain information is essential for improving the
performance of various downstream tasks on complex terrains, especially for the
locomotion and navigation of legged robots. We present a novel framework for
neural urban terrain reconstruction with uncertainty estimations. It generates
dense robot-centric elevation maps online from sparse LiDAR observations. We
design a novel pre-processing and point features representation approach that
ensures high robustness and computational efficiency when integrating multiple
point cloud frames. A Bayesian-GAN model then recovers the detailed terrain
structures while simultaneously providing the pixel-wise reconstruction
uncertainty. We evaluate the proposed pipeline through extensive simulation and
real-world experiments. It demonstrates efficient terrain reconstruction with
high quality and real-time performance on a mobile platform, which further
benefits the downstream tasks of legged robots. (See
https://kin-zhang.github.io/ndem/ for more details.)
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