In outdoor environments, ground segmentation is an important pre-processing task for the local environment perception of autonomous vehicle platforms and is the basis for obstacle detection, classification, and path planning. However, existing ground segmentation algorithms primarily focus on dense point cloud data and often struggle to achieve satisfactory accuracy when applied to sparse point cloud data. In light of this challenge, we propose a novel ground segmentation method, LR-Seg, specifically tailored for sparse point cloud data. Our method aims to address the limitations observed in previous approaches and provide improved performance, particularly on sparse point cloud data. To achieve faster processing speed, the method first divides the original point cloud into sub-regions of different sizes based on the distribution characteristics of the sparse point cloud data in the XOY plane. Then, the point clouds are appropriately assigned, and most of the non-ground points in each sub-region are removed using the PCA plane fitting method. Finally, the point cloud geometric feature information within each sub-region is used to reduce over-segmentation. The experimental results show that our method can perform ground segmentation quickly on flat and slope road sections, and the average F1 score can be maintained above 90% with an average time of 3 ms, which is suitable for various road environments.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    LR-Seg: A Ground Segmentation Method for Low-Resolution LiDAR Point Clouds


    Beteiligte:
    Mijit, Tayir (Autor:in) / Firkat, Eksan (Autor:in) / Yuan, Xiaming (Autor:in) / Liang, Yutong (Autor:in) / Zhu, Jihong (Autor:in) / Hamdulla, Askar (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.01.2024


    Format / Umfang :

    10209238 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    SCSSnet: Learning Spatially-Conditioned Scene Segmentation on LiDAR Point Clouds

    Rist, Christoph B. / Schmidt, David / Enzweiler, Markus et al. | IEEE | 2020


    Efficient Segmentation of 3D LIDAR Point Clouds Handling Partial Occlusion

    Aue, J. / Langer, D. / Mueller-Bessler, B. et al. | British Library Conference Proceedings | 2011


    Efficient segmentation of 3D LIDAR point clouds handling partial occlusion

    Aue, Jan / Langer, D. / Muller-Bessler, B. et al. | IEEE | 2011


    Road Markings Segmentation from LIDAR Point Clouds using Reflectivity Information

    Certad, Novel / Morales-Alvarez, Walter / Olaverri-Monreal, Cristina | IEEE | 2022


    RangeSeg: Range-Aware Real Time Segmentation of 3D LiDAR Point Clouds

    Chen, Tzu-Hsuan / Chang, Tian Sheuan | IEEE | 2022