Indoor Parking Localization Based on Dual Weighted Particle Filter

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


For successful autonomous valet parking, accurate knowledge of vehicle location in the global map is crucial. Using sensors and maps can be an alternative method for the indoor parking localization to compensate for lack of GPS coverage. However, maps only have static elements such as walls and pillars and semi-static elements such as parked vehicles are not included. Thus, sensor data rarely match with the map data due to the semi-static elements. We developed a robust localization algorithm using a laser scanner, a static map and a feature extraction algorithm. Overall map features consisted of the center positions of parking slots and pillars. Parking slot measurements were extracted from parked vehicles. Position estimation errors occur when matching the vehicle center positions to the parking slot center positions. This error can be reduced by detecting pillars and giving more weight to the observation of these static elements. The contribution of this approach is that we can use not only static objects but also semi-static objects and that position estimation error is reduced. The algorithm was evaluated in the scaled down indoor parking model. The average position errors of this algorithm are compared with errors of odometry data and SIS particle filter.

Original languageEnglish
Pages (from-to)293-298
Number of pages6
JournalInternational Journal of Precision Engineering and Manufacturing
Issue number2
Publication statusPublished - 2018 Feb 1

Bibliographical note

Funding Information:
This work was jointly supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2017R1A2A1A17069329) and the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program, No. 10067184, ‘Development of armored robot systems for personal protections of rescuemen and emergency management operations in the composite disaster site’.

Publisher Copyright:
© 2018, Korean Society for Precision Engineering and Springer-Verlag GmbH Germany, part of Springer Nature.


  • Autonomous parking
  • Dual weighting
  • Feature extraction
  • Localization
  • Particle filter

ASJC Scopus subject areas

  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering


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