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

  • 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.

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Author:Gjorgji Nikolovski, Michael RekeORCiD, Ingo ElsenORCiD, Stefan SchifferORCiD
Parent Title (English):2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)
Document Type:Conference Proceeding
Year of Completion:2021
Date of the Publication (Server):2022/01/24
Tag:3D object detection; Deep learning; LiDAR; Three-dimensional displays; autonomous driving
First Page:236
Last Page:242
2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops), 11-17 July 2021,  Nagoya, Japan.
Institutes:FH Aachen / Fachbereich Elektrotechnik und Informationstechnik
FH Aachen / ECSM European Center for Sustainable Mobility