MOSU: マルチモーダルシーン理解による自律長距離ロボットナビゲーション
MOSU: Autonomous Long-range Robot Navigation with Multi-modal Scene Understanding
LiDAR・画像セグメンテーション・視覚言語モデルを統合し、社会的規範も考慮した屋外長距離自律ナビゲーションシステムを実現した。
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著者: Jing Liang, Kasun Weerakoon, Daeun Song, Senthurbavan Kirubaharan, Xuesu Xiao, Dinesh Manocha
分類: cs.RO
原文アブストラクト
We present MOSU, a novel autonomous long-range navigation system that enhances global navigation for mobile robots through multimodal perception and on-road scene understanding. MOSU addresses the outdoor robot navigation challenge by integrating geometric, semantic, and contextual information to ensure comprehensive scene understanding. The system combines GPS and QGIS map-based routing for high-level global path planning and multi-modal trajectory generation for local navigation refinement. For trajectory generation, MOSU leverages multi-modalities: LiDAR-based geometric data for precise obstacle avoidance, image-based semantic segmentation for traversability assessment, and Vision-Language Models (VLMs) to capture social context and enable the robot to adhere to social norms in complex environments. This multi-modal integration improves scene understanding and enhances traversability, allowing the robot to adapt to diverse outdoor conditions. We evaluate our system in real-world on-road environments and benchmark it on the GND dataset, achieving a 10% improvement in traversability on navigable terrains while maintaining a comparable navigation distance to existing global navigation methods.
