手描きスケッチマップによる未知環境ナビゲーションの学習
SkeNa: Learning to Navigate Unseen Environments Based on Abstract Hand-Drawn Maps
手描きのスケッチマップだけを頼りに未知環境で目的地を目指すナビゲーションタスクSkeNaを提案し、大規模データセットSoRと手法SkeNavigatorを構築した。
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著者: Haojun Xu, Jiaqi Xiang, Wu Wei, Jinyu Chen, Linqing Zhong, Linjiang Huang, Hongyu Yang, Si Liu
分類: cs.RO, cs.AI
原文アブストラクト
A typical human strategy for giving navigation guidance is to sketch route maps based on the environmental layout. Inspired by this, we introduce Sketch map-based visual Navigation (SkeNa), an embodied navigation task in which an agent must reach a goal in an unseen environment using only a hand-drawn sketch map as guidance. To support research for SkeNa, we present a large-scale dataset named SoR, comprising 54k trajectory and sketch map pairs across 71 indoor scenes. In SoR, we introduce two navigation validation sets with varying levels of abstraction in hand-drawn sketches, categorized based on their preservation of spatial scales in the environment, to facilitate future research. To construct SoR, we develop an automated sketch-generation pipeline that efficiently converts floor plans into hand-drawn representations. To solve SkeNa, we propose SkeNavigator, a navigation framework that aligns visual observations with hand-drawn maps to estimate navigation targets. It employs a Ray-based Map Descriptor (RMD) to enhance sketch map valid feature representation using equidistant sampling points and boundary distances. To improve alignment with visual observations, a Dual-Map Aligned Goal Predictor (DAGP) leverages the correspondence between sketch map features and on-site constructed exploration map features to predict goal position and guide navigation. SkeNavigator outperforms prior floor plan navigation methods by a large margin, improving SPL on the high-abstract validation set by 105% relatively. Our code and dataset will be released.
