OpenStreetMap-based Autonomous Navigation With LiDAR Naive-Valley-Path Obstacle Avoidance

TL;DR

This paper introduces a novel LiDAR-based Naive-Valley-Path method for autonomous navigation using OpenStreetMap data, achieving accurate, obstacle-avoiding driving in complex environments with high efficiency.

Contribution

The paper presents a new LiDAR-based path planning approach that leverages OSM data and valley concepts to improve local path accuracy and obstacle avoidance in autonomous navigation.

Findings

Successfully navigated over 20 km with 0.24m average error

Achieved 19.8 ms average sample time for real-time operation

Effectively avoided static and dynamic obstacles in complex environments

Abstract

OpenStreetMaps (OSM) is currently studied as the environment representation for autonomous navigation. It provides advantages such as global consistency, a heavy-less map construction process, and a wide variety of road information publicly available. However, the location of this information is usually not very accurate locally. In this paper, we present a complete autonomous navigation pipeline using OSM information as environment representation for global planning. To avoid the flaw of local low-accuracy, we offer the novel LiDAR-based Naive-Valley-Path (NVP) method that exploits the concept of "valley" areas to infer the local path always furthest from obstacles. This behavior allows navigation always through the center of trafficable areas following the road's shape independently of OSM error. Furthermore, NVP is a naive method that is highly sample-time-efficient. This time efficiency also enables obstacle avoidance, even for dynamic objects. We demonstrate the system's robustness in our research platform BLUE, driving autonomously across the University of Alicante Scientific Park for more than 20 km with 0.24 meters of average error against the road's center with a 19.8 ms of average sample time. Our vehicle avoids static obstacles in the road and even dynamic ones, such as vehicles and pedestrians.