Range Image-based LiDAR Localization for Autonomous Vehicles
This addresses the problem of precise localization for autonomous vehicles in large-scale outdoor environments, representing an incremental improvement through a novel integration of existing techniques.
The paper tackles robust map-based localization for autonomous vehicles by using range images from LiDAR scans and synthetic views from a mesh-based map, proposing a new observation model integrated into a Monte Carlo framework, which achieves reliable and accurate localization across different environments and operates online at the sensor frame rate.
Robust and accurate, map-based localization is crucial for autonomous mobile systems. In this paper, we exploit range images generated from 3D LiDAR scans to address the problem of localizing mobile robots or autonomous cars in a map of a large-scale outdoor environment represented by a triangular mesh. We use the Poisson surface reconstruction to generate the mesh-based map representation. Based on the range images generated from the current LiDAR scan and the synthetic rendered views from the mesh-based map, we propose a new observation model and integrate it into a Monte Carlo localization framework, which achieves better localization performance and generalizes well to different environments. We test the proposed localization approach on multiple datasets collected in different environments with different LiDAR scanners. The experimental results show that our method can reliably and accurately localize a mobile system in different environments and operate online at the LiDAR sensor frame rate to track the vehicle pose.