Recognizing traversable terrain from 3D point cloud data is critical, as it directly impacts the performance of autonomous navigation in off-road environments. However, existing segmentation algorithms often struggle with challenges related to changes in data distribution, environmental specificity, and sensor variations. Moreover, when encountering sunken areas, their performance is frequently compromised, and they may even fail to recognize them. To address these challenges, we introduce B-TMS, a novel approach that performs map-wise terrain modeling and segmentation by utilizing Bayesian generalized kernel (BGK) within the graph structure known as the tri-grid field (TGF). Our experiments encompass various data distributions, ranging from single scans to partial maps, utilizing both public datasets representing urban scenes and off-road environments, and our own dataset acquired from extremely bumpy terrains. Our results demonstrate notable contributions, particularly in terms of robustness to data distribution variations, adaptability to diverse environmental conditions, and resilience against the challenges associated with parameter changes.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    B-TMS: Bayesian Traversable Terrain Modeling and Segmentation Across 3D LiDAR Scans and Maps for Enhanced Off-Road Navigation


    Beteiligte:
    Oh, Minho (Autor:in) / Shin, Gunhee (Autor:in) / Jang, Seoyeon (Autor:in) / Lee, Seungjae (Autor:in) / Lee, Dongkyu (Autor:in) / Song, Wonho (Autor:in) / Yu, Byeongho (Autor:in) / Lim, Hyungtae (Autor:in) / Lee, Jaeyoung (Autor:in) / Myung, Hyun (Autor:in)


    Erscheinungsdatum :

    02.06.2024


    Format / Umfang :

    4156395 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    LiDAR Based Traversable Regions Identification Method for Off-Road UGV Driving

    Shan, Yunxiao / Fu, Yao / Chen, Xiangchun et al. | IEEE | 2024


    LISEG: LIGHTWEIGHT ROAD-OBJECT SEMANTIC SEGMENTATION IN 3D LIDAR SCANS FOR AUTONOMOUS DRIVING

    Zhang, Wenquan / Zhou, Chancheng / Yang, Junjie et al. | British Library Conference Proceedings | 2018


    LiSeg: Lightweight Road-object Semantic Segmentation In 3D LiDAR Scans For Autonomous Driving

    Zhang, Wenquan / Zhou, Chancheng / Yang, Junjie et al. | IEEE | 2018


    Fast terrain traversability estimation with terrestrial lidar in off-road autonomous navigation

    Goodin, Christopher / Dabbiru, Lalitha / Hudson, Christopher et al. | SPIE | 2021


    Terrain navigation using Bayesian statistics

    Bergman, N. / Ljung, L. / Gustafsson, F. | Tema Archiv | 1999