Abstract
Environmental perception is one of the key issues in robot research. To complete more precise tasks, robots need a recognition ability to recognize all dynamic and static objects in the environment accurately. Currently, the prevailing approach in robotics is to employ object detection methods for perceiving stationary objects in the robot's vicinity. This primarily entails the use of 2D object detection based on images and 3D object detection based on depth images or point cloud data. From an input data perspective, it is evident that depth images and point cloud data offer a substantially richer set of three-dimensional spatial information compared to conventional image data. Therefore, 3D object detection is more suitable for robot environmental perception. This article provides an overview of 3D object detection methods for facial robots. This article mainly reviews methods based on radar point cloud information, introduces the historical evolution of 3D object detection, and elaborates on the performance and limitations of different methods. At the same time, it lists commonly used datasets for related tasks.
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References
Ko, D.-K., Lee, K.-W., Lee, D.H., Lim, S.-C.: Vision-based interaction force estimation for robot grip motion without tactile/force sensor. Expert Syst. Appl. 211, 118441 (2023). https://doi.org/10.1016/j.eswa.2022.118441
Wang, J., et al.: Field effect transistor-based tactile sensors: from sensor configurations to advanced applications. InfoMat. 5, e12376 (2023). https://doi.org/10.1002/inf2.12376
Liu, L., et al.: MixTeacher: Mining promising labels with mixed scale teacher for semi-supervised object detection. In: Presented at the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2023)
Gonçalves, C.B., Souza, J.R., Fernandes, H.: CNN architecture optimization using bio-inspired algorithms for breast cancer detection in infrared images. Comput. Biol. Med. 142, 105205 (2022). https://doi.org/10.1016/j.compbiomed.2021.105205
Abumalloh, R.A., et al.: Medical image processing and COVID-19: aA literature review and bibliometric analysis. J. Infect. Public Health 15, 75–93 (2022). https://doi.org/10.1016/j.jiph.2021.11.013
He, C., Li, R., Zhang, Y., Li, S., Zhang, L.: MSF: motion-guided sequential fusion for efficient 3d object detection from point cloud sequences. In: Presented at the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2023)
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3d classification and segmentation. In: Presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
Feng, C., Jie, Z., Zhong, Y., Chu, X., Ma, L.: AeDet: azimuth-invariant multi-view 3d object detection. In: Presented at the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2023)
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: dDeep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems. Curran Associates, Inc. (2017)
Shi, S., Wang, Z., Shi, J., Wang, X., Li, H.: From points to parts: 3D object detection from point cloud with part-aware and part-aggregation network. IEEE Trans. Pattern Anal. Mach. Intell. 43, 2647–2664 (2021). https://doi.org/10.1109/TPAMI.2020.2977026
Yang, Z., Sun, Y., Liu, S., Shen, X., Jia, J.: STD: sparse-to-dense 3D object detector for point cloud. In: Presented at the Proceedings of the IEEE/CVF International Conference on Computer Vision (2019)
Vaswani, A., et al/: Attention is all you need. In: Advances in Neural Information Processing Systems. Curran Associates, Inc. (2017)
Pan, X., **a, Z., Song, S., Li, L.E., Huang, G.: 3D object detection with pointformer. In: Presented at the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2021)
Shi, S., Wang, Z., Wang, X., Li, H.: Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud. (2019)
Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: PointPillars: fast encoders for object detection from point clouds. In: Presented at the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019)
Li, Y., et al.: Voxel Field Fusion for 3D Object Detection. In: Presented at the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022)
Acknowledgement
This work is supported by the Bei**g Natural Science Foundation (No. 4222025), the National Natural Science Foundation of China (No. 61931012), the Bei**g Municipal Science and Technology (No. Z221100000222016), and QIYUAN LAB Innovation Foundation (Innovation Research) Project (No. S20210201107).
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Li, M., Ma, N. (2024). Overview of 3D Object Detection for Robot Environment Perception. In: You, P., Liu, S., Wang, J. (eds) Proceedings of International Conference on Image, Vision and Intelligent Systems 2023 (ICIVIS 2023). ICIVIS 2023. Lecture Notes in Electrical Engineering, vol 1163. Springer, Singapore. https://doi.org/10.1007/978-981-97-0855-0_64
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DOI: https://doi.org/10.1007/978-981-97-0855-0_64
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