Hünermund, M., Schütz, A., Groneberg, M. (2021) LiDAR-Sensoren. In: Sackewitz, Michael (ed.) Leitfaden zur optischen 3D-Messtechnik. Fraunhofer Verlag, Stuttgart.Search in Google Scholar
Karlsson, B., Karlsson, N. and Wide, P. (2000) A dynamic safety system based on sensor fusion. Journal of Intelligent Manufacturing, 11(5), 475-483.10.1023/A:1008922330419Search in Google Scholar
Tan, J.T.C., Arai, T. (2011) Triple stereo vision system for safety monitoring of human-robot collaboration in cellular manufacturing. In: 2011 IEEE International Symposium on Assembly and Manufacturing (ISAM), IEEE, 1-6.10.1109/ISAM.2011.5942335Search in Google Scholar
Frese, C., Fetzner, A. and Frey, C. (2014) Multi-sensor obstacle tracking for safe human-robot interaction. In: 41st International Symposium on Robotics, ISR/Robotik 2014, VDE, 1-8.Search in Google Scholar
Moel, A. (2022) How Collaborative Is Your Manufacturing Application? Quality Magazine, 61(2), BNP Media.Search in Google Scholar
Li, B. (2017) 3D fully convolutional network for vehicle detection in point cloud. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 1513-1518.10.1109/IROS.2017.8205955Search in Google Scholar
Guo, Y., Wang, H., Hu, Q., Liu, H., Liu, L. and Bennamoun, M. (2020) Deep learning for 3D point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence, 43(12), 4338-4364.Search in Google Scholar
Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J. and Beijbom, O. (2019) Pointpillars: Fast encoders for object detection from point clouds. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 12697-12705.Search in Google Scholar
Simon, M., Milz, S., Amende, K., Gross, H.M. (2018) Complex-YOLO: Real-time 3D Object Detection on Point Clouds. arXiv preprint arXiv:1803.06199.Search in Google Scholar
Zheng, W., Tang, W., Jiang, L. and Fu, C.W. (2021) SE-SSD: Self-ensembling single-stage object detector from point cloud. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 14494-14503.Search in Google Scholar
Petschnigg, C. and Pilz, J. (2021) Uncertainty estimation in deep neural networks for point cloud segmentation in factory planning. Modelling, 2(1), 1-17.10.3390/modelling2010001Search in Google Scholar
Braham, M., Lejeune, A., Droogenbroeck, M. V. (2014) A physically motivated pixel-based model for background subtraction in 3D images. In: International Conference on 3D Imaging (IC3D), Liège, Belgium, 6-7.10.1109/IC3D.2014.7032591Search in Google Scholar
Hünermund, M., Groneberg, M., Schütz, A. (2021) Fast Connected Components Object Segmentation on Fused Lidar and Stereo-Camera Point Clouds with Visual-Inertial-Gimbal for Mobile Applications Utilizing GPU Acceleration. In: Reliability and Statistics in Transportation and Communication, Bd. 195. Cham: Springer International Publishing (Lecture Notes in Networks and Systems), 73–83.10.1007/978-3-030-68476-1_7Search in Google Scholar
ANSI B11.TR10-2020: Functional Safety of Artificial Intelligence for Machinery Applications (2020).Search in Google Scholar
Borkman, S., Crespi, A., Dhakad, S., Ganguly, S., Hogins, J., Jhang, Y. (2021) Unity Perception: Generate Synthetic Data for Computer Vision. Online: http://arxiv.org/pdf/2107.04259v2, pp. 6-7.Search in Google Scholar
Geiger, A., Lenz, P., Stiller, C., Urtasun, R. (2017) 3D Object Detection Evaluation 2017. Karslruhe Institute of Technology, last checked 04.08.2022, available online at http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d.Search in Google Scholar
Wang, X., Song, J. (2021) ICIoU: Improved Loss Based on Complete Intersection Over Union for Bounding Box Regression, IEEE Access, 9, 105686-105695.10.1109/ACCESS.2021.3100414Search in Google Scholar