A SLAM with simultaneous construction of 2D and 3D maps based on Rao-Blackwellized particle filters

Yao Li, Fan Zhun, Zhu Guijie, Li Wenji, Li Chong, Wang Yupeng, Xie Honghui

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

8 Citations (Scopus)

Abstract

This paper presents a SLAM (Simultaneous Localization and Mapping) method which builds 2D grid maps and generates the OctoMap based on Rao-Blackwellized particle filters. This work combines wheeled odometry and laser scan with particle filter algorithm to get the pose of the robot, and at the same time fuses the data of depth camera to generate OctoMap, OctoMap is an integrated open source framework based on octree, which is well known for its memory efficiency for the representation of 3D environments. The traditional 3D point cloud map cannot be applied in robot navigation. But OctoMap is a 3D occupancy grid mapping, which can be applied to 3D path planning of flying robots and other robots that are equipped with manipulators. In short, the experimental results demonstrate that the proposed methods can make a robot to synchronize building 2D and 3D maps very efficiently.

Original languageEnglish
Title of host publicationProceedings - 2018 10th International Conference on Advanced Computational Intelligence, ICACI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages374-378
Number of pages5
ISBN (Electronic)9781538643624
DOIs
Publication statusPublished - 8 Jun 2018
Event10th International Conference on Advanced Computational Intelligence, ICACI 2018 - Xiamen, Fujian, China
Duration: 29 Mar 201831 Mar 2018

Publication series

NameProceedings - 2018 10th International Conference on Advanced Computational Intelligence, ICACI 2018

Conference

Conference10th International Conference on Advanced Computational Intelligence, ICACI 2018
Country/TerritoryChina
CityXiamen, Fujian
Period29/03/1831/03/18

Keywords

  • 2D grid map
  • Gmapping
  • OctoMap
  • Rao-blackwellized particle filters
  • SLAM

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