Map switching Monte Carlo LiDAR localization for automated driving in parking garages

A highly accurate and reliable localization system is one of the keystones for automated driving in underground parking garages. Indoor parking areas pose specific challenges due to the narrow spaces, repetitive architectural layout, low lighting conditions and the unavailability of Global navigation satellite system (GNSS). To solve this challenge, we propose a novel Map Switching Monte Carlo Localization (MSMCL) approach based on a single close-to-production front-mounted LiDAR with a Field of View (FOV) of 110 degrees. To localize the vehicle, we use the correlation of LiDAR measurements with a $2\mathbf{D}$reference map, with different switchable representations. The measurement distribution is computed in advance in the Fourier space, which decouples the calculation time from the number of particles. We compare our approach to a custom variant of a state-of-the-art Iterative Closest Point (ICP) algorithm. In an experimental evaluation, the ICP and MSMCL approaches achieve a median angular accuracy of 0.31° and 0.36° resp. as well as a median total position accuracy of 12.0cm and 16.5cm resp. The 95 percentile total positioning accuracy is 35.7cm for ICP and 38.5cm for MSMCL. While ICP achieves a higher accuracy, the proposed MSMCL approach can solve the lost-robot problem, i.e. estimate the vehicle position even if no initial guess is available. Both ICP and MSMCL can be operated at real-time with a frequency of at least 5Hz. We validated the localization system by successfully completing multiple fully automated test drives in the Fraunhofer FOKUS parking garage with a prototype Mercedes E-Class W213.