In this paper, we extend the Monte Carlo Localization formulation for a more efficient global localization using coarse digital maps (for instance, the OpenStreetMap maps). The proposed formulation uses the map constraints in order to reduce the state dimension, which is ideal for a Monte Carlo-based particle filter. Also, we propose including to the data association process the matching of the traffic signals’ information to the road properties, so that their exact position do not need to be previously mapped for updating the filter. In the proposed approach, no low-level point cloud mapping was required and neither the use of LIDAR data. The experiments were conducted using a dataset collected by the CARINA II intelligent vehicle and the results suggest that the method is adequate for a localization pipeline. The dataset is available online and the code is available on GitHub.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Monte Carlo particle filter formulation for mapless-based localization


    Contributors:


    Publication date :

    2022-06-05


    Size :

    985757 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A Monte-Carlo Localization Algorithm Based on Iterated Cubature Particle Filter

    Liu, Y. / Su, J. / Zhu, M. | British Library Online Contents | 2013



    FreeNavi: Landmark-Based Mapless Indoor Navigation Based on WiFi Fingerprints

    Guo, Yao / Wang, Wenjun / Chen, Xiangqun | IEEE | 2017


    CGAN-based System Approach for Mapless Driving on Highways

    Michalke, Thomas / Krauss, Sebastian | IEEE | 2024


    Development and Implementation of a Mapless, Model Based SCR Control System

    Chavannavar, Praveen | British Library Conference Proceedings | 2014