Rapid Localization and Mapping Method Based on Adaptive Particle Filters

Anas Charroud, Karim El Moutaouakil, Ali Yahyaouy, Uche Onyekpe, Vasile Palade, Md Nazmul Huda

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
11 Downloads (Pure)


With the development of autonomous vehicles, localization and mapping technologies have become crucial to equip the vehicle with the appropriate knowledge for its operation. In this paper, we extend our previous work by prepossessing a localization and mapping architecture for autonomous vehicles that do not rely on GPS, particularly in environments such as tunnels, under bridges, urban canyons, and dense tree canopies. The proposed approach is of two parts. Firstly, a K-means algorithm is employed to extract features from LiDAR scenes to create a local map of each scan. Then, we concatenate the local maps to create a global map of the environment and facilitate data association between frames. Secondly, the main localization task is performed by an adaptive particle filter that works in four steps: (a) generation of particles around an initial state (provided by the GPS); (b) updating the particle positions by providing the motion (translation and rotation) of the vehicle using an inertial measurement device; (c) selection of the best candidate particles by observing at each timestamp the match rate (also called particle weight) of the local map (with the real-time distances to the objects) and the distances of the particles to the corresponding chunks of the global map; (d) averaging the selected particles to derive the estimated position, and, finally, using a resampling method on the particles to ensure the reliability of the position estimation. The performance of the newly proposed technique is investigated on different sequences of the Kitti and Pandaset raw data with different environmental setups, weather conditions, and seasonal changes. The obtained results validate the performance of the proposed approach in terms of speed and representativeness of the feature extraction for real-time localization in comparison with other state-of-the-art methods.
Original languageEnglish
Article number9439
Issue number23
Early online date2 Dec 2022
Publication statusE-pub ahead of print - 2 Dec 2022

Bibliographical note

Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://


  • autonomous driving
  • mapping
  • feature extraction
  • Memory
  • SLAM
  • self-driving vehicles
  • localization
  • Knowledge
  • Reproducibility of Results
  • Algorithms
  • Rotation


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