DR. Nav: Semantic-Geometric Representations for Proactive Dead-End Recovery and Navigation

TL;DR

DR. Nav is a proactive navigation system that predicts dead-ends and plans safe routes in unstructured environments by fusing sensor data and continuously updating risk assessments, improving safety and efficiency.

Contribution

It introduces a unified semantic-geometric approach for dead-end prediction and recovery, integrating risk-aware planning without prior environment maps.

Findings

Achieved 83.33% higher dead-end detection accuracy

Reduced time-to-goal by 52.4% compared to state-of-the-art methods

Effectively handles complex indoor and outdoor scenarios

Abstract

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