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

In this paper, we present a complete autonomous navigation pipeline for unstructured outdoor environments. The main contribution of this work is on the path planning module, which we divided into two main categories: Global Path Planning (GPP) and Local Path Planning (LPP). For environment representation, instead of complex and heavy grid maps, the GPP layer uses road network information obtained directly from OpenStreetMaps (OSM). In the LPP layer, we use a novel Naive-Valley-Path (NVP) method to generate a local path avoiding obstacles in the road in realtime. This approach uses a naive representation of the local environment using a LiDAR sensor. Also, it uses a naive optimization that exploits the concept of ”valley” areas in the cost map. We demonstrate the system’s robustness experimentally in our research platform BLUE, driving autonomously across the University of Alicante Scientific Park for more than 20 km in a 12.33 ha area.

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