Skip to main content

Object Detection by Tiny-YOLO on TurtleBot3 as an Educational Robot

  • 149 Accesses

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 689)


By the unceasing efforts of many scientists, machine learning algorithms are increasing quickly with an enhanced object detection performance for the explicit applications on specific devices. This paper investigates the operation of TurtleBot3 as an educational wheeled autonomous robot which Tiny-YOLO algorithm has been applied as a novel application on it. At first, the required knowledge about the kinematics and dynamics of a differential drive robot with constraints is studied, and a graph where the robot is working under some specific conditions is plotted. The robot operating system (ROS) and simultaneous localization and mapping (SLAM) of the TurtleBot3 have been explained. The robot is equipped with a LiDAR sensor and a camera embedded onto it as a sensor fusion SLAM for a more precise type of mapping. Finally, Tiny-YOLO as a machine learning algorithm for object detection is implemented based on the fact that Raspberry Pi could not handle the full YOLO due to its memory capacity limitation, and the results are discussed.


  • TurtleBot3
  • Machine learning
  • Tiny-YOLO
  • SLAM
  • ROS

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions


  1. Chernousko F (2017) Locomotion principles for mobile robotic systems. Proc Comput Sci: 213–216

    Google Scholar 

  2. Siegwart R, Nourbakhsh IR, Scaramuzza AD (2004) Introduction to autonomous mobile robots, pp 13–43, 369–393

    Google Scholar 

  3. Moezzi R, Krcmarik D, Hlava J, Cýrus J (2020) Hybrid SLAM modelling of autonomous robot with augmented reality device. Mater Today Proc 32:103–107.

    CrossRef  Google Scholar 

  4. Moezzi R, Krcmarik D, Cýrus J, Bahri H, Koci J (2022) Object detection using Microsoft HoloLens by a single forward propagation CNN. In: Proceedings in adaptation, learning and optimization, vol 15. Springer, Cham.

  5. Wang W, Lai Q, Fu H, Shen J, Ling H, Yang R (2022) Salient object detection in the deep learning era: an in-depth survey. IEEE Trans Pattern Anal Mach Intell 44(6):3239–3259.

    CrossRef  Google Scholar 

  6. Cai Z, Vasconcelos N (2018) Cascade R-CNN: delving into high quality object detection. Paper presented at the proceedings of the IEEE computer society conference on computer vision and pattern recognition, 6154–6162.

  7. Girshick R (2015) Fast R-CNN. Paper presented at the proceedings of the IEEE international conference on computer vision, 2015 international conference on computer vision, ICCV 2015, 1440–1448.

  8. Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. Paper presented at the advances in neural information processing systems, 2015-Jan, 91–99

    Google Scholar 

  9. Fan H, Ling H (2019) Siamese cascaded region proposal networks for real-time visual tracking. Paper presented at the proceedings of the IEEE computer society conference on computer vision and pattern recognition, 2019-June, 7944–7953.

  10. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. Paper presented at the proceedings of the IEEE computer society conference on computer vision and pattern recognition, 2016-Dec, 779–788.

  11. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C, Berg AC (2016) SSD: single shot multibox detector.

  12. Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. Paper presented at the proceedings—30th IEEE conference on computer vision and pattern recognition, CVPR 2017, 2017-Jan, 6517–6525.

  13. Tumas P, Serackis A (2018) Automated image annotation based on YOLOv3. Paper presented at the 2018 IEEE 6th workshop on advances in information, electronic and electrical engineering, AIEEE 2018—proceedings.

  14. Wai YJ, Yussof ZM, Bin Salim SI, Chuan LK (2018) Fixed point implementation of tiny-yolo-v2 using OpenCL on FPGA. Int J Adv Comput Sci Appl 9(10):506–512

    Google Scholar 

  15. Ma J, Chen L, Gao Z (2018) Hardware implementation and optimization of tiny-YOLO network.

  16. Features of Turtlebot3, Robotis [online]. Available

  17. ROS—Robot Operating System [online]. Available Zugriff am 20 2 2022

  18. Hernandez-Mendez S, Hernandez-Mejia C, Herrera Olea DB, Marin-Hernandez A, Rios-Figueroa HV (2021) Path planning simulation of a quadrotor in ROS/Gazebo using RGPPM. Paper presented at the proceedings—2021 international conference on mechatronics, electronics and automotive engineering, ICMEAE 2021, 20–25.

  19. Moezzi R, Krcmarik D, Bahri H, Hlava J (2019) Autonomous vehicle control based on HoloLens technology and raspberry pi platform: an educational perspective. Paper presented at the IFAC-PapersOnLine, 52(27), 80–85.

  20. Gazebo Navigation Simulation, Robotis [online]. Available Zugriff am 15 2 2022.

  21. Bonaccorso G (2017) Machine learning algorithms. Packt Publishing, pp 6–23

    Google Scholar 

  22. Fu H, Niu Z, Zhang C, Chen JMAJ (2013) Visual cortex inspired CNN model for feature construction in text analysis. PubMed, Beijing, pp 1–7

    Google Scholar 

  23. Huang R, Pedoeem J, Chen C (2019) YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers. Paper presented at the proceedings—2018 IEEE international conference on big data. Big data 2018, 2503–2510.

  24. Lin TY et al (2014) Microsoft COCO: common objects in context. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) Computer vision—ECCV 2014. ECCV 2014. Lecture notes in computer science, vol 8693. Springer, Cham.

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Reza Moezzi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Moezzi, R., Saw, A., Bischoff, S., Cyrus, J., Hlava, J. (2023). Object Detection by Tiny-YOLO on TurtleBot3 as an Educational Robot. In: Sharma, H., Shrivastava, V., Bharti, K.K., Wang, L. (eds) Communication and Intelligent Systems. ICCIS 2022. Lecture Notes in Networks and Systems, vol 689. Springer, Singapore.

Download citation