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.

翻译：OpenStreetMap（OSM）当前被研究作为自主导航的环境表示方法。该方法具有全局一致性、地图构建过程轻量化以及公开可用的丰富道路信息等优势。然而，这些信息的位置通常存在局部精度不足的问题。本文提出完整的自主导航流水线，采用OSM信息作为环境表示进行全局规划。为克服局部低精度缺陷，我们提出新型的基于LiDAR的朴素谷径（NVP）方法，该方法利用"谷"区域概念，始终规划离障碍物最远的局部路径。这种特性使得导航能始终沿道路形状、穿越可通行区域中心进行，不受OSM误差影响。此外，NVP是一种高效采样时间的方法。这种时间效率使其能够实现避障功能，包括对动态物体的避让。我们在研究平台BLUE上验证了系统的鲁棒性：该平台在阿利坎特大学科技园区自主行驶超过20公里，平均偏离道路中心误差为0.24米，平均采样时间为19.8毫秒。我们的车辆能够避让道路上的静态障碍物，甚至包括车辆和行人等动态障碍物。