Enhanced simultaneous localization and map building

Tung Yuan Lin, Chen Chien Hsu, Wei Yen Wang, Yin Tien Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


FastSLAM is a well-known algorithm with its purpose to process the simultaneous localization and mapping (SLAM). There are two main FastSLAM algorithms, i.e., FastSLAM 1.0 and FastSLAM 2.0. However, the speed of execution is too slow due to the superabundant comparisons of every single existing landmarks. Thus computationally efficient SLAM (CESLAM) was presented to deal with the problem and to achieve the goal of real-time processing design. Nevertheless, there is a great possibility that large errors may occur, because the original CESLAM only takes odometer information to estimate the robot's pose in particles. Therefore, this paper not only utilizes the odometer information but also the measurement information from sensors. Finally, simulation results are illustrated that the modified version of CESLAM algorithm can effectively ameliorate the accuracy of localization and mapping.

Original languageEnglish
Title of host publicationCACS 2014 - 2014 International Automatic Control Conference, Conference Digest
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781479945849
Publication statusPublished - 2014 Apr 28
Event2014 International Automatic Control Conference, CACS 2014 - Kaohsiung, Taiwan
Duration: 2014 Nov 262014 Nov 28

Publication series

NameCACS 2014 - 2014 International Automatic Control Conference, Conference Digest


Other2014 International Automatic Control Conference, CACS 2014


  • FastSLAM
  • extended Kalman filter
  • particle filter

ASJC Scopus subject areas

  • Control and Systems Engineering


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