Reduction of LiDAR Point Cloud Maps for Localization of Resource-Constrained Robotic Systems

Chungjae Choe, Sangmin Ahn, Nakju Doh, Changjoo Nam

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


This article presents a method for mobile robotic systems to reduce the size of 3-D point cloud maps while achieving small localization errors even though the reduced maps are used for localization. Localization with dense depth maps especially in large environments could be computationally expensive, so not affordable for robotic systems with limited computational resources. Since such point cloud data collected using a multichannel 3-D LiDAR have representational redundancy, some amount of the data could be removed for compactness. However, existing methods demand annotations and descriptors defined by human experts or lose important spatial features for accurate localization. We propose a fully-automatic reduction method for reliable navigation of mobile systems with low computing power and limited storage. Given a full 3-D point cloud map, our method generates a graph whose vertices and edges represent robot poses and the similarity between the data collected from a pair of robot poses, respectively. Each robot pose is associated with a point cloud set. We find a dominating set of the graph, which is a subset of robot poses such that the robot can localize reliably only with the point clouds collected from those poses. Experimental results with indoor environments show that our system reduces the amount of map data up to 93% while not exceeding acceptable ranges of average translation and rotation errors, which are 0.25 m and 0.5°, respectively.

Original languageEnglish
Pages (from-to)916-927
Number of pages12
JournalIEEE Systems Journal
Issue number1
Publication statusPublished - 2023 Mar 1

Bibliographical note

Publisher Copyright:
© 2007-2012 IEEE.


  • Autonomous vehicle navigation
  • localization
  • mapping

ASJC Scopus subject areas

  • Information Systems
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Networks and Communications
  • Computer Science Applications


Dive into the research topics of 'Reduction of LiDAR Point Cloud Maps for Localization of Resource-Constrained Robotic Systems'. Together they form a unique fingerprint.

Cite this