Data fusion plays a significant role in autonomous driving domain. Using an efficient combination of sensors like LiDAR, radar, and cameras could determine how quickly and accurately a vehicle makes all kinds of decisions related to road safety. In this article, we propose two approaches to improve object distance estimation by combining camera and LiDAR sensors. This work is inspired by the work presented in [2]. We propose to use instance segmentation and hierarchical clustering algorithms to resolve estimation errors generated when two or several bounding boxes (bbox) of detected objects overlap with each other. KITTI and Waymo databases were used to evaluate the accuracy of the proposed approaches. Finally, we compare the accuracy of our approaches with the accuracy proposed in [2] for some specific scenarios.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Improving Object Distance Estimation in Automated Driving Systems Using Camera Images, LiDAR Point Clouds and Hierarchical Clustering


    Beteiligte:


    Erscheinungsdatum :

    11.07.2021


    Format / Umfang :

    1227854 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    An Optimal Hierarchical Clustering Approach to Mobile LiDAR Point Clouds

    Xu, Sheng / Wang, Ruisheng / Wang, Hao et al. | IEEE | 2020


    /LiDAR AUTOMATED OBJECT ANNOTATION USING FUSED CAMERA/LiDAR DATA POINTS

    LIU ZHONGTAO / ESPER JAMES / LEE JONG HO | Europäisches Patentamt | 2025

    Freier Zugriff

    /LiDAR AUTOMATED OBJECT ANNOTATION USING FUSED CAMERA/LiDAR DATA POINTS

    LIU ZHONGTAO / ESPER JAMES / LEE JONG HO | Europäisches Patentamt | 2023

    Freier Zugriff


    Top-down object detection from LiDAR point clouds

    SMOLYANSKIY NIKOLAI / OLDJA RYAN / CHEN KE et al. | Europäisches Patentamt | 2024

    Freier Zugriff