Abstract
The recent advances in deep learning models have enabled the robotics community to utilize their potential. The mobile robot domain on which deep learning has the most influence is scene understanding. Scene understanding enables mobile robots to exist and execute their tasks through processes such as object detection, semantic segmentation, or instance segmentation. A perception system that can recognize and locate objects in the scene is of the highest importance for achieving autonomous behavior of robotic systems. Having that in mind, we develop the mobile robot perception system based on deep learning. More precisely, we utilize an accurate and fast Convolution Neural Network (CNN) model to enable a mobile robot to detect objects in its scene in a real-time manner. The integration of two CNN models (SSD and MobileNet) is performed and implemented on mobile robot RAICO (Robot with Artificial Intelligence based COgnition). The experimental results show that the proposed perception system enables a high degree of object recognition with satisfying inference speed, even with limited processing power provided by Nvidia Jetson Nano integrated within RACIO.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Sünderhauf, N., et al.: The limits and potentials of deep learning for robotics. Int. J. Robot. Res. 37(4–5), 405–420 (2018)
Lee, C.Y., Lee, H., Hwang, I., Zhang, B.T.: Visual perception framework for an intelligent mobile robot. In: 17th International Conference on Ubiquitous Robots (UR), pp. 612–616 (2020)
Young, J., Basile, V., Kunze, L., Cabrio, E., Hawes, N.: Towards lifelong object learning by integrating situated robot perception and semantic web mining. In: 22nd European Conference on Artificial Intelligence (ECAI), vol. 285, pp. 1458–1466. (2016)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2016)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. ArXiv Preprint arXiv:1704.04861 (2017)
Arabi, S., Haghighat, A., Sharma, A: A deep learning based solution for construction equipment detection: from development to deployment. ArXiv Preprint arXiv:1904.09021 (2019)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Acknowledgment
This research has been financially supported by the Ministry of Education, Science and Technological Development of the Serbian Government, through the project “Integrated research in macro, micro, and nano mechanical engineering – Deep learning of intelligent manufacturing systems in production engineering” (contract No. 451-03-9/2021-14/200105), and by the Science Fund of the Republic of Serbia, Grant No. 6523109, AI – MISSION 4.0, 2020–2022.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Jokić, A., Petrović, M., Miljković, Z. (2022). Real-Time Mobile Robot Perception Based on Deep Learning Detection Model. In: Karabegović, I., Kovačević, A., Mandžuka, S. (eds) New Technologies, Development and Application V. NT 2022. Lecture Notes in Networks and Systems, vol 472. Springer, Cham. https://doi.org/10.1007/978-3-031-05230-9_80
Download citation
DOI: https://doi.org/10.1007/978-3-031-05230-9_80
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-05229-3
Online ISBN: 978-3-031-05230-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)