Autonomous Navigation in Unstructured Environments: A Probabilistic Approach for Generating Local Guidance With Limited Prior Information

Abstract

Generating reliable local guidance information is crucial for autonomous navigation in unstructured environments with limited prior information. Conventional approaches often fuse navigation cues with different physical semantics into a single scalar objective through manually weighted cost terms, making the contribution of each cue difficult to interpret and often requiring scenario-specific parameter tuning. To address this issue, we propose a probabilistic local reference path generation method that uniformly models environmental traversability, goal direction, kinematic preference, and temporal consistency as angular distributions in a polar coordinate system. Their agreement with environmental evidence is quantified by the Bhattacharyya Coefficient to derive adaptive fusion weights. On top of the fused posterior, anchor inference is formulated as a dynamic-programming-based sequence optimization over selected radial layers, enabling real-time generation of smooth local reference paths in continuous space. We conducted simulation experiments on the BARN dataset, where the proposed method significantly outperforms baseline methods in terms of task completion rate, while maintaining competitive performance in metrics such as path length, smoothness, and safety. Furthermore, we conducted real-world vehicle experiments, including autonomous driving trials that covered 3.2 km in real off-road scenarios, which further validate the effectiveness and practicality of the method under limited prior information.