Three-dimensional iterative closest point-based outdoor SLAM using terrain classification

Yong Ju Lee, Jae Bok Song

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


To navigate in an unknown environment, a robot should build a model for the environment. For outdoor environments, an elevation map is used as the main world model. We considered the outdoor simultaneous localization and mapping (SLAM) method to build a global elevation map by matching local elevation maps. In this research, the iterative closest point (ICP) algorithm was used to match local elevation maps and estimate a robot pose. However, an alignment error is generated by the ICP algorithm due to false selection of corresponding points. Therefore, we propose a new method to classify environmental data into several groups, and to find the corresponding points correctly and improve the performance of the ICP algorithm. Different weights are assigned according to the classified groups because certain groups are very sensitive to the viewpoint of the robot. Three-dimensional (3-D) environmental data acquired by tilting a 2-D laser scanner are used to build local elevation maps and to classify each grid of the map. Experimental results in real environments show the increased accuracy of the proposed ICP-based matching and a reduction in matching time.

Original languageEnglish
Pages (from-to)147-158
Number of pages12
JournalIntelligent Service Robotics
Issue number2
Publication statusPublished - 2011 Apr

Bibliographical note

Funding Information:
This research was supported by a grant from the David and Lucille Packard Foundation under grant number 98-3314; we are grateful for the same. The authors collectively acknowledge the fruitful discussions with M. D. Mesarovic that enhanced the research.


  • Elevation map
  • Mapping
  • Outdoor environments
  • SLAM

ASJC Scopus subject areas

  • Computational Mechanics
  • Engineering (miscellaneous)
  • Mechanical Engineering
  • Artificial Intelligence


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