Vision-based vanishing point detection of autonomous navigation of mobile robot for outdoor applications

Leonard Rusli, Brilly Nurhalim, Rusman Rusyadi

Abstract

The vision-based approach to mobile robot navigation is considered superior due to its affordability. This paper aims to design and construct an autonomous mobile robot with a vision-based system for outdoor navigation. This robot receives inputs from camera and ultrasonic sensor. The camera is used to detect vanishing points and obstacles from the road. The vanishing point is used to detect the heading of the road. Lines are extracted from the environment using a canny edge detector and Houghline Transforms from OpenCV to navigate the system. Then, removed lines are processed to locate the vanishing point and the road angle. A low pass filter is then applied to detect a vanishing point better. The robot is tested to run in several outdoor conditions such as asphalt roads and pedestrian roads to follow the detected vanishing point. By implementing a Simple Blob Detector from OpenCV and ultrasonic sensor module, the obstacle's position in front of the robot is detected. The test results show that the robot can avoid obstacles while following the heading of the road in outdoor environments. Vision-based vanishing point detection is successfully applied for outdoor applications of autonomous mobile robot navigation.



Keywords


Houghline transform; road lines detection; vanishing point determination; simple blob detector.

Full Text:

PDF


References


Cherubini, F. Spindler, & F. Chaumette. 2012. A new tentacles-based technique for avoiding obstacles during visual navigation. in Proc. IEEE International Conference on Robotics and Automation (ICRA). DOI:10.1109/ICRA.2012.6224584

Yeh, H. G., Wang, R., and Ary, J. 2016. “Automatic Driving System by Recognizing Road Signs Using Digital Image Processing.” ProQuest Dissertations Publishing, 10196473, pp. 1-28. Doi: 10.17265/2328-2223/2018.02.007

Kondo, Yuki & Numada, Munetoshi & Koshimizu, Hiroyasu & Yoshida, Ichiro. (2018). A Study on Fast and Robust Vanishing Point Detection System Using Fast M-Estimation Method and Regional Division for In-vehicle Camera. J. of Electrical Engineering. 6. 10.17265/2328-2223/2018.02.007.

G. Yang, Y. Wang, J. Yang and Z. Lu, "Fast and Robust Vanishing Point Detection Using Contourlet Texture Detector for Unstructured Road," in IEEE Access, vol. 7, pp. 139358-139367, 2019, doi: 10.1109/ACCESS.2019.2944244.

Han, J., Yang, Z., Hu, G. et al. Accurate and Robust Vanishing Point Detection Method in Unstructured Road Scenes. J Intell Robot Syst 94, 143–158 (2019). https://doi.org/10.1007/s10846-018-0814-8.

Khac, C.N.; Choi, Y.; Park, J.H.; Jung, H.-Y. A Robust Road Vanishing Point Detection Adapted to the Real-world Driving Scenes. Sensors 2021, 21, 2133. https://doi.org/10.3390/s21062133.

Yu, Z.; Zhu, L. Roust Vanishing Point Detection Based on the Combination of Edge and Optical Flow. In Proceedings of the 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), Nagoya, Japan, 13–15 July 2019; pp. 184–188, Doi: 10.1109/ACIRS.2019.8936016.

Yang, W.; Fang, B.; Tang, Y.Y. Fast and Accurate Vanishing Point Detection and Its Application in Inverse Perspective Mapping of Structured Road. IEEE Trans. Syst. Man Cybern. Syst. 2018, 48, 755–766, Doi: 10.1109/TSMC.2016.2616490.

Yong, Li & Ding, Weili & XuGuang, Zhang & Ju, Zhaojie. (2016). Road detection algorithm for Autonomous Navigation Systems based on dark channel prior and vanishing point in complex road scenes. Robotics and Autonomous Systems. 85. 10.1016/j.robot.2016.08.003.

Shichen Liu, Yichao Zhou, Yajie Zhao; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 12859-12868

C. Chang, J. Zhao and L. Itti, "DeepVP: Deep Learning for Vanishing Point Detection on 1 Million Street View Images," 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018, pp. 4496-4503, doi: 10.1109/ICRA.2018.8460499.

H. Kong, J. Y. Audibert, & J. Ponce. August 2010. General road detection from a single image. IEEE Transactions on Image Processing, vol. 19, no. 8, pp. 2211–2220, https://doi.org/10.1109/TIP.2010.2045715

O. Miksik. May 2012. Rapid vanishing point estimation for general road detection. in Proc. IEEE International Conference on Robotics and Automation (ICRA), doi: 10.1109/ICRA.2012.6225206

C. Siagian, C. K. Chang, R. Voorhies, & L. Itti. March/April 2011. Beobot 2.0: Cluster architecture for mobile robotics, Journal of Field Robotics, vol. 28, no. 2, pp. 278–302, Doi: 10.1002/rob.20379

Siagian, Christian & Chang, Chin-Kai & Itti, Laurent. 2013. Mobile robot navigation system in outdoor pedestrian environment using vision-based road recognition. Proceedings - IEEE International Conference on Robotics and Automation. 564-571. 10.1109/ICRA.2013.6630630.

Tokuda, N., Funahashi, T., Numada, M., and Koshimizu, H. 2012. “The Line Detection by Hough Transform for Vanishing Point Detection.” Presented at ViEW2012, IS1-C6, Dec. 2012, Doi: 10.17265/2328-2223/2018.02.007


Article Metrics

Metrics Loading ...

Metrics powered by PLOS ALM

Refbacks

  • There are currently no refbacks.




Copyright (c) 2021 Journal of Mechatronics, Electrical Power, and Vehicular Technology

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

 

Cited-By

1. Multi-Scale Fully Convolutional Network-Based Semantic Segmentation for Mobile Robot Navigation
Thai-Viet Dang, Ngoc-Tam Bui
Electronics  vol: 12  issue: 3  first page: 533  year: 2023  
doi: 10.3390/electronics12030533