Detection of Principle Directions in Unknown Environments for Autonomous Navigation

Autonomous navigation in unknown but wellstructured environments (e.g., parking lots) is a common task for human drivers and an important goal for autonomous vehicles. In such environments, the vehicles must obey the standard conventions of driving (e.g., passing oncoming vehicles on the correct side), but often lack a map that can be used to guide path planning in an appropriate way. The robots must therefore rely on features of the environment to drive in a safe and predictable way. In this work, we focus on detecting one of such features, the principal directions of the environment. We propose a Markov-random-field (MRF) model for estimating the maximum-likelihood field of principal directions, given the local linear features extracted from the vehicle’s sensor data, and show that the method leads to robust estimates of principal directions in complex real-life driving environments. We also demonstrate how the computed principal directions can be used to guide a path-planning algorithm, leading to the generation of significantly improved trajectories.