TY  - CHAP
A1  - Nikolovski, Gjorgji
A1  - Reke, Michael
A1  - Elsen, Ingo
A1  - Schiffer, Stefan
T1  - Machine learning based 3D object detection for navigation in unstructured environments
T2  - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)
N2  - 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.
KW  - 3D object detection
KW  - LiDAR
KW  - autonomous driving
KW  - Deep learning
KW  - Three-dimensional displays
Y1  - 2021
SN  - 978-1-6654-7921-9
U6  - http://dx.doi.org/10.1109/IVWorkshops54471.2021.9669218
N1  - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops), 11-17 July 2021,  Nagoya, Japan.
SP  - 236
EP  - 242
PB  - IEEE
ER  -