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

TL;DR

This paper introduces a hybrid particle filtering and scan-matching method for LIDAR-only vehicle localization, utilizing a likelihood grid and parallel processing to achieve near real-time performance in large 3D maps.

Contribution

It presents a novel hybrid approach combining particle filters with global-local scan matching and a pre-computed likelihood grid for efficient LIDAR-based localization.

Findings

Achieves near real-time localization on KITTI datasets.

Reduces computational load compared to traditional particle filters.

Demonstrates effective vehicle pose estimation in large 3D environments.

Abstract

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