Range Image-based LiDAR Localization for Autonomous Vehicles
arXiv:2105.12121 · doi:10.1109/ICRA48506.2021.9561335
Abstract
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.
Accepted by ICRA 2021. Code: https://github.com/PRBonn/range-mcl. arXiv admin note: text overlap with arXiv:2105.11717
References in corpus (6)
- Open3D: A Modern Library for 3D Data Processing
- SuMa++: Efficient LiDAR-based Semantic SLAM
- OverlapNet: Loop Closing for LiDAR-based SLAM
- Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans
- Learning to Localize Using a LiDAR Intensity Map
- Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU
Cited by in corpus (7)
- Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data
- OverlapTransformer: An Efficient and Rotation-Invariant Transformer Network for LiDAR-Based Place Recognition
- IR-MCL: Implicit Representation-Based Online Global Localization
- Online Range Image-based Pole Extractor for Long-term LiDAR Localization in Urban Environments
- Rmagine: 3D Range Sensor Simulation in Polygonal Maps via Raytracing for Embedded Hardware on Mobile Robots
- MICP-L: Mesh-based ICP for Robot Localization using Hardware-Accelerated Ray Casting
- Monocular Camera Localization for Automated Vehicles Using Image Retrieval