Real-Time Point Cloud Alignment for Vehicle Localization in a High Resolution 3D Map

  • Balázs NagyEmail author
  • Csaba Benedek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11129)


In this paper we introduce a Lidar based real time and accurate self localization approach for self driving vehicles (SDV) in high resolution 3D point cloud maps of the environment obtained through Mobile Laser Scanning (MLS). Our solution is able to robustly register the sparse point clouds of the SDVs to the dense MLS point cloud data, starting from a GPS based initial position estimation of the vehicle. The main steps of the method are robust object extraction and transformation estimation based on multiple keypoints extracted from the objects, and additional semantic information derived from the MLS based map. We tested our approach on roads with heavy traffic in the downtown of a large city with large GPS positioning errors, and showed that the proposed method enhances the matching accuracy with an order of magnitude. Comparative tests are provided with various keypoint selection strategies, and against a state-of-the-art technique.


Lidar Point cloud Registration Scene understanding 



This work was supported by the National Research, Development and Innovation Fund (grants NKFIA K-120233 and KH-125681), and by the Széchenyi 2020 Program (grants EFOP-3.6.2-16-2017-00013 and 3.6.3-VEKOP-16- 2017-00002). Cs. Benedek also acknowledges the support of the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Machine Perception Research LaboratoryInstitute for Computer Science and ControlBudapestHungary
  2. 2.Faculty of Information Technology and BionicsPázmány Péter Catholic UniversityBudapestHungary

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