DR. Nav: 行き止まりからの能動的復帰とナビゲーションのための意味・幾何表現
DR. Nav: Semantic-Geometric Representations for Proactive Dead-End Recovery and Navigation
RGB-LiDAR融合とベイズ推定で行き止まりの可能性と復帰地点をリアルタイムに推定し、リスクを考慮したコストマップで安全な経路計画を実現するナビゲーション手法。
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著者: Vignesh Rajagopal, Kasun Weerakoon Kulathun Mudiyanselage, Gershom Devake Seneviratne, Pon Aswin Sankaralingam, Mohamed Elnoor, Jing Liang, Rohan Chandra, Dinesh Manocha
分類: cs.RO
原文アブストラクト
We present DR. Nav (Dead-End Recovery-aware Navigation), a novel approach to autonomous navigation in scenarios where dead-end detection and recovery are critical, particularly in unstructured environments where robots must handle corners, vegetation occlusions, and blocked junctions. DR. Nav introduces a proactive strategy for navigation in unmapped environments without prior assumptions. Our method unifies dead-end prediction and recovery by generating a single, continuous, real-time semantic cost map. Specifically, DR. Nav leverages cross-modal RGB-LiDAR fusion with attention-based filtering to estimate per-cell dead-end likelihoods and recovery points, which are continuously updated through Bayesian inference to enhance robustness. Unlike prior mapping methods that only encode traversability, DR. Nav explicitly incorporates recovery-aware risk into the navigation cost map, enabling robots to anticipate unsafe regions and plan safer alternative trajectories. We evaluate DR. Nav across multiple dense indoor and outdoor scenarios and demonstrate an increase of 83.33% in accuracy in detection, a 52.4% reduction in time-to-goal (path efficiency), compared to state-of-the-art planners such as DWA, MPPI, and Nav2 DWB. Furthermore, the dead-end classifier functions
