  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.
