Environment analysis of automatic vehicles needs the detection from 3-D point cloud information. This paper addresses this task when only partial scanning data are available. Our method develops the detection capabilities of autonomous vehicles equipped with 3-D range sensors for navigation purposes. In industrial practice, the safety scanners of automated guided vehicles (AGVs) and a localization technology provide an additional possibility to gain 3-D point clouds from planar contour points or low vertical resolution. Based on this data and a suitable evaluation algorithm, intelligence of vehicles can be significantly increased without the need for installation of additional sensors. In this paper, we propose a solution for an obstacle categorization problem for partial point clouds without shape modeling. The approach is tested for a known database, as well as for real-life scenarios. In case of AGVs, real-time run is provided by on-board computers of usual complexity.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Obstacle Prediction for Automated Guided Vehicles Based on Point Clouds Measured by a Tilted LIDAR Sensor


    Beteiligte:
    Rozsa, Zoltan (Autor:in) / Sziranyi, Tamas (Autor:in)


    Erscheinungsdatum :

    01.08.2018


    Format / Umfang :

    2870100 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    DALi: Domain Adaptation in LiDAR Point Clouds for 3D Obstacle Detection

    Cortes, Irene / Beltran, Jorge / De La Escalera, Arturo et al. | IEEE | 2022


    Lidar-Based Obstacle Detection and Path Prediction for Unmanned Surface Vehicles

    Zantopp, Nico / Budak, Akif / Delea, Cosmin et al. | Fraunhofer Publica | 2024

    Freier Zugriff

    Lidar-Based Obstacle Detection and Path Prediction for Unmanned Surface Vehicles

    Zantopp, Nico / Budak, Akif / Delea, Cosmin et al. | DataCite | 2024


    AUTOMATED VISIBILITY FIELD EVALUATION OF TRAFFIC SIGN BASED ON 3D LIDAR POINT CLOUDS

    S. Zhang / C. Wang / M. Cheng et al. | DOAJ | 2019

    Freier Zugriff

    MODULAR SENSOR SYSTEM FOR AUTOMATED GUIDED VEHICLES

    LIOTTA NICHOLAS / ANSELL MICHAEL / SLAVIK EITAN et al. | Europäisches Patentamt | 2023

    Freier Zugriff