不確かさ推定を伴う都市地形のリアルタイムニューラル高密度標高マッピング
Real-time Neural Dense Elevation Mapping for Urban Terrain with Uncertainty Estimations
疎なLiDAR観測からロボット中心の高密度標高マップをオンライン生成し、Bayesian-GANで地形構造を復元しつつ画素ごとの不確かさを推定するフレームワークを提案。
著者: Bowen Yang, Qingwen Zhang, Ruoyu Geng, Lujia Wang, Ming Liu
分類: cs.RO
原文アブストラクト
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.)
