Enhanced Simultaneous Localization and Mapping (ESLAM) for Mobile Robots

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


FastSLAM, such as FastSLAM 1.0 and FastSLAM 2.0, is a popular algorithm to solve the simultaneous localization and mapping (SLAM) problem for mobile robots. In real environments, however, the execution speed by FastSLAM would be too slow to achieve the objective of real-time design with a satisfactory accuracy because of excessive comparisons of the measurement with all the existing landmarks in particles, particularly when the number of landmarks is drastically increased. In this paper, an enhanced SLAM (ESLAM) is proposed, which uses not only odometer information but also sensor measurements to estimate the robot's pose in the prediction step. Landmark information that has the maximum likelihood is then used to update the robot's pose before updating the landmarks' location. Compared to existing FastSLAM algorithms, the proposed ESLAM algorithm has a better performance in terms of computation efficiency as well as localization and mapping accuracy as demonstrated in the illustrated examples.

Original languageEnglish
Article number1750007
JournalInternational Journal of Humanoid Robotics
Issue number2
Publication statusPublished - 2017 Jun 1


  • FastSLAM
  • SLAM
  • Simultaneous localization and mapping
  • extended Kalman filter
  • navigation
  • particle filter

ASJC Scopus subject areas

  • Mechanical Engineering
  • Artificial Intelligence


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