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This paper deals with the development of a localization methodology forautonomous vehicles using only a $3\Dim$ LIDAR sensor. In the context of thispaper, localizing a vehicle in a known 3D global map of the environment isessentially to find its global $3\Dim$ pose (position and orientation) withinthis map. The problem of tracking is then to use sequential LIDAR scanmeasurement to also estimate other states such as velocity and angular rates,in addition to the global pose of the vehicle. Particle filters are often usedin localization and tracking, as in applications of simultaneously localizationand mapping. But particle filters become computationally prohibitive with theincrease in particles, often required to localize in a large $3\Dim$ map.Further, computing the likelihood of a LIDAR scan for each particle is initself a computationally expensive task, thus limiting the number of particlesthat can be used for real time performance. To this end, we propose a hybridapproach that combines the advantages of a particle filter with a global-localscan matching method to better inform the re-sampling stage of the particlefilter. Further, we propose to use a pre-computed likelihood grid to speedupthe computation of LIDAR scans. Finally, we develop the complete algorithm toextensively leverage parallel processing to achieve near sufficient real-timeperformance on publicly available KITTI datasets.

Scan-Matching based Particle Filtering approach for LIDAR-only Localization