円形アクセス可能深度：UGVナビゲーションのための堅牢な走行可能性表現
Circular Accessible Depth: A Robust Traversability Representation for UGV Navigation
LiDAR点群から極座標系に基づく走行可能領域の境界を予測するCAD表現と、注意機構付き多フレーム融合モジュールSAMを備えたニューラルネットワークCADNetを提案し、半教師あり学習で実環境のUGVナビゲーションを実現した。
著者: Shikuan Xie, Ran Song, Yuenan Zhao, Xueqin Huang, Yibin Li, Wei Zhang
分類: cs.RO, cs.CV
原文アブストラクト
In this paper, we present the Circular Accessible Depth (CAD), a robust traversability representation for an unmanned ground vehicle (UGV) to learn traversability in various scenarios containing irregular obstacles. To predict CAD, we propose a neural network, namely CADNet, with an attention-based multi-frame point cloud fusion module, Stability-Attention Module (SAM), to encode the spatial features from point clouds captured by LiDAR. CAD is designed based on the polar coordinate system and focuses on predicting the border of traversable area. Since it encodes the spatial information of the surrounding environment, which enables a semi-supervised learning for the CADNet, and thus desirably avoids annotating a large amount of data. Extensive experiments demonstrate that CAD outperforms baselines in terms of robustness and precision. We also implement our method on a real UGV and show that it performs well in real-world scenarios.
