In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Machine learning based 3D object detection for navigation in unstructured environments


    Beteiligte:
    Nikolovski, Gjorgji (Autor:in) / Reke, Michael (Autor:in) / Elsen, Ingo (Autor:in) / Schiffer, Stefan (Autor:in)


    Erscheinungsdatum :

    2021-07-11


    Format / Umfang :

    1666073 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Pedestrian Lane Detection in Unstructured Environments for Assistive Navigation

    Le, Manh Cuong / Phung, Son Lam / Bouzerdoum, Abdesselam | IEEE | 2014


    Topological Data Analysis for Navigation in Unstructured Environments

    Pinapala, Amith / Mollan, Calahan / Pandey, Vijitashwa | SAE Technical Papers | 2023


    A solution for a mobile robot navigation into unstructured environments

    Vitabile,S. / Bianco,F. / Sorbello,F. et al. | Kraftfahrwesen | 1997


    A Solution for a Mobile Robot Navigation into Unstructured Environments

    Vitabile, S. / Bianco, F. / Sorbello, F. | British Library Conference Proceedings | 1997


    Object Detection and 6D Pose Estimation for Precise Robotic Manipulation in Unstructured Environments

    di Castro, Mario / Camarero Vera, Jorge / Ferre, Manuel et al. | Springer Verlag | 2019