Machine learning based 3D object detection for navigation in unstructured environments. Nikolovski, G., Reke, M., Elsen, I., & Schiffer, S. In 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops), pages 236–242, July, 2021.
Machine learning based 3D object detection for navigation in unstructured environments [link]Ieeexplore  doi  abstract   bibtex   
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.
@INPROCEEDINGS{ Nikolovski:etAl_IV2021WS_ML3D-ObjDet,
  author       = {Nikolovski, Gjorgji and Reke, Michael and Elsen, Ingo and Schiffer, Stefan},
  booktitle    = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)},
  title        = {{Machine learning based 3D object detection for navigation in unstructured environments}},
  year         = {2021},
  pages        = {236--242},
  abstract     = {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.},
  keywords     = {ADP; ARTUS; UPNS4D; Deep learning; Training; Solid
                  modeling; Three-dimensional displays; Annotations;
                  Conferences; Neural networks; 3D object detection;
                  LiDAR; autonomous driving},
  doi          = {10.1109/IVWorkshops54471.2021.9669218},
  url_IEEExplore = {https://ieeexplore.ieee.org/abstract/document/9669218},
  ID_IEEE      = {9669218},
  month        = {July},
}
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