In this paper, we present a novel method for recognizing pole-like objects in LiDAR point clouds, which is useful for landmark-based localization and high-definition (HD) map generation. Our method utilizes a state-of-the-art deep neural network relying on learned encodings of the point cloud input. Here, we modified an existing network architecture to improve the detection of small objects such as poles. To enable the estimation of bounding cylinders for pole-like objects, we propose a respective object anchor design with an accompanying strategy for matching ground truth objects to object anchors during network training. Furthermore, we examine the impact of two different data representations of the point cloud on the detection performance, as well as the impact of topological alternatives. The performance of our method is demonstrated on a dataset including various challenging classes of poles. We plan to publish this dataset as part of this work, which fills a gap regarding publicly available LiDAR point cloud datasets covering various elements of HD maps such as pole-like objects. Our method achieves a mean recall, precision, and classification accuracy of 0.85, 0.85, and 0.93, respectively, and may serve as a future baseline for other approaches.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    DNN-Based Recognition of Pole-Like Objects in LiDAR Point Clouds


    Contributors:


    Publication date :

    2021-09-19


    Size :

    2855943 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    TRACKING OBJECTS IN LIDAR POINT CLOUDS WITH ENHANCED TEMPLATE MATCHING

    LI DALONG / SMITH ALEX M / HORTON STEPHEN | European Patent Office | 2020

    Free access

    Tracking objects in LIDAR point clouds with enhanced template matching

    LI DALONG / SMITH ALEX M / HORTON STEPHEN | European Patent Office | 2021

    Free access

    Image-based classification of small flying objects detected in LiDAR point clouds

    Hammer, Marcus / Borgmann, Björn / Hebel, Marcus et al. | SPIE | 2020


    AUTOMATIC LABELING OF OBJECTS FROM LIDAR POINT CLOUDS VIA TRAJECTORY-LEVEL REFINEMENT

    YANG ANQI JOYCE / CASAS ROMERO SERGIO / DVORNIK MIKITA et al. | European Patent Office | 2024

    Free access

    EFFECTIVE RAILROAD FRAGMENTATION AND INFRASTRUCTURE RECOGNITION BASED ON DENSE LIDAR POINT CLOUDS

    M. Cserép / A. Demján / F. Mayer et al. | DOAJ | 2022

    Free access