GND: 屋外キャンパス環境におけるマルチモーダル知覚と多カテゴリ走行可能性を備えたグローバルナビゲーションデータセット
GND: Global Navigation Dataset with Multi-Modal Perception and Multi-Category Traversability in Outdoor Campus Environments
10の大学キャンパスから3D LiDAR点群・RGB/360度画像と歩道・車道・階段・オフロード・障害物の走行可能性マップを収集し、地図ベース/地図なしナビゲーションや場所認識に応用可能な大規模データセットを構築した。
著者: Jing Liang, Dibyendu Das, Daeun Song, Md Nahid Hasan Shuvo, Mohammad Durrani, Karthik Taranath, Ivan Penskiy, Dinesh Manocha, Xuesu Xiao
分類: cs.RO
原文アブストラクト
Navigating large-scale outdoor environments requires complex reasoning in terms of geometric structures, environmental semantics, and terrain characteristics, which are typically captured by onboard sensors such as LiDAR and cameras. While current mobile robots can navigate such environments using pre-defined, high-precision maps based on hand-crafted rules catered for the specific environment, they lack commonsense reasoning capabilities that most humans possess when navigating unknown outdoor spaces. To address this gap, we introduce the Global Navigation Dataset (GND), a large-scale dataset that integrates multi-modal sensory data, including 3D LiDAR point clouds and RGB and 360-degree images, as well as multi-category traversability maps (pedestrian walkways, vehicle roadways, stairs, off-road terrain, and obstacles) from ten university campuses. These environments encompass a variety of parks, urban settings, elevation changes, and campus layouts of different scales. The dataset covers approximately 2.7km2 and includes at least 350 buildings in total. We also present a set of novel applications of GND to showcase its utility to enable global robot navigation, such as map-based global navigation, mapless navigation, and global place recognition.
