Benchmarking Particle Filter Algorithms for Efficient Velodyne-Based Vehicle Localization
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Blanco Claraco, José Luis; Mañas, Francisco; Torres-Moreno, José Luis; Rodríguez, Francisco; Giménez Fernández, AntonioFecha
2019-07Resumen
Keeping a vehicle well-localized within a prebuilt-map is at the core of any autonomous
vehicle navigation system. In this work, we show that both standard SIR sampling and rejection-based
optimal sampling are suitable for efficient (10 to 20 ms) real-time pose tracking without feature
detection that is using raw point clouds from a 3D LiDAR. Motivated by the large amount of
information captured by these sensors, we perform a systematic statistical analysis of how many
points are actually required to reach an optimal ratio between efficiency and positioning accuracy.
Furthermore, initialization from adverse conditions, e.g., poor GPS signal in urban canyons, we also
identify the optimal particle filter settings required to ensure convergence. Our findings include
that a decimation factor between 100 and 200 on incoming point clouds provides a large savings in
computational cost with a negligible loss in localization accuracy for a VLP-16 scanner. Furthermore,
an initial densi...