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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:


    Publication date :

    2021-07-11


    Size :

    1666073 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Optical Navigation in Unstructured Dynamic Railroad Environments

    Burschka, Darius / Robl, Christian / Ohrendorf-Weiss, Sebastian | ArXiv | 2020

    Free access

    Topological Data Analysis for Navigation in Unstructured Environments

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


    Topological Data Analysis for Navigation in Unstructured Environments

    Mollan, Calahan / Pandey, Vijitashwa / Pinapala, Amith | British Library Conference Proceedings | 2023



    Learning terrain segmentation with classifier ensembles for autonomous robot navigation in unstructured environments

    Procopio, M. J. / Mulligan, J. / Grudic, G. | British Library Online Contents | 2009