3D Pose Recognition System of Dump Truck for Autonomous Excavator
Source abstract
Abstract
The purpose of an excavator is to dig up materials and load them onto heavy-duty dump trucks. Typically, an excavator is positioned at the rear of the dump truck when loading. In order to automate this process, this paper proposes a system that employs a combined stereo camera and two LiDAR sensors to determine the three-dimensional (3D) position of the truck’s cargo box and to analyze its loading space. Sparse depth information acquired from the two LiDAR sensors is used to detect points on the door of the cargo box and establish the plane on its rear side. Dense depth information of the cargo box acquired from the stereo camera sensor is projected onto the plane of the box’s rear to estimate its initial 3D position. In the next step, the point cloud sampled along the long shaft of the edge of the cargo box is used as the input of the Iterative Closest Point algorithm to calculate a more accurate cargo box position. The data collected from the stereo camera are then used to determine the 3D position of the cargo box and provide an estimate of the volume of the load along with the 3D position of the loading space to the excavator. In order to demonstrate the efficiency of the proposed method, a mock-up of a heavy-duty truck cargo box was created, and the volume of the load in the cargo box was analyzed.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · 3D Surveying and Cultural Heritage · Optical measurement and interference techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science and Engineering, Kyungpook National University, 80 Daehak-ro, Puk-gu, Daegu 41566, Korea
School of Electronics and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Puk-gu, Daegu 41566, Korea
School of Electronic Engineering, Kyungpook National University, 80 Daehak-ro, Puk-gu, Daegu 41566, Korea
References (27)
Show source-provided references
1. The practical usage of i-construction for enhancing the safety of the construction site
2. (2022, February 02). Volvo CE Automattion Journey. Available online: https://www.youtube.com/watch?v=FRh2QZaVwoQ.
3. (2022, February 02). Hyundai Construction Equipment. Available online: http://www.hyundai-ce.com/ko/innovation/ai.
4. Development of PC210LCi-10/PC200i-10 machine control hydraulic excavator
5. Dynamic model of excavator10.1061/(ASCE)0893-1321(1993)6:2(148) ↗
6. Zhao, J., Long, P., Wang, L., Qian, L., Lu, F., Song, X., Manocha, D., and Zhang, L. (2020). AES: Autonomous excavator system for re-al-world and hazardous environments. arXiv.
7. Automated excavator based on reinforcement learning and multibody system dynamics10.1109/ACCESS.2020.3040246 ↗
8. Shariati, H., Yeraliyev, A., Terai, B., Tafazoli, S., and Ramezani, M. (2019, January 16–17). Towards autonomous mining via intelligent excavators. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.
9. Schmidt, D., Proetzsch, M., and Berns, K. (2010, January 3–7). Simulation and control of an autonomous bucket excavator for landscaping tasks. Proceedings of the 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, USA.10.1109/ROBOT.2010.5509546 ↗
10. Guan, T., He, Z., Manocha, D., and Zhang, L. (2021). TTM: Terrain traversability mapping for autonomous excavator navigation in unstructured environments. arXiv.10.15607/RSS.2022.XVIII.049 ↗
11. Development of a 3D local terrain modeling system of intelligent excavation robot10.1007/s12205-016-0309-8 ↗
12. A robotic excavator for autonomous truck loading10.1023/A:1008914201877 ↗
13. Borthwick, J.R. (2009). Mining haul truck pose estimation and load profiling using stereo vision. [PhD Thesis, University of British Columbia].
14. Yoshida, H., Yoshimoto, T., Umino, D., and Mori, N. (2021, January 23–27). Practical Full Automation of Excavation and Loading for Hydraulic Excavators in Indoor Environments. Proceedings of the 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), Lyon, France.10.1109/CASE49439.2021.9551504 ↗
15. Sugasawa, Y., Chikushi, S., Komatsu, R., Kasahara, J.L., Pathak, S., Yajima, R., and Asama, H. (2020, January 29–31). Visualization of Dump Truck and Excavator in Bird’s-eye View by Fisheye Cameras and 3D Range Sensor. Proceedings of the 16th International Conference on Intelligent Autonomous Systems (IAS), Singapore.
16. Chai, Z., Sun, Y., and Xiong, Z. (2018, January 9–12). Novel method for LiDAR camera calibration by plane fitting. Proceedings of the 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Auckland, New Zealand.10.1109/AIM.2018.8452339 ↗
17. A method for the solution of certain problems in least squares10.1002/cnm.1324 ↗
18. Fischler, M.A., and Bolles, R.C. (1981). Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography, ACM.10.1145/358669.358692 ↗
19. Hirschmuller, H. (2006, January 17–22). tereo vision in structured environments by consistent semi-global matching. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), New York, NY, USA.
20. Konolige, K. (2010, January 3–7). Projected texture stereo. Proceedings of the 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, USA.10.1109/ROBOT.2010.5509796 ↗
21. Desjardins, D., and Payeur, P. (2007, January 28–30). Dense stereo range sensing with marching pseudo-random patterns. Proceedings of the Fourth Canadian Conference on Computer and Robot Vision (CRV’07), Montreal, QC, Canada.10.1109/CRV.2007.22 ↗
22. Rusinkiewicz, S., and Levoy, M. (June, January 28). Efficient variants of the ICP algorithm. Proceedings of the Third International Conference on 3-D Digital Imaging and Modeling, Quebec City, QC, Canada.
23. Generalized-icp
24. (2022, February 02). Dump Trucks|Volvo Trucks USA. Available online: https://www.volvotrucks.us/trucks/applications/dump/.
25. (2022, February 02). Scania. Available online: https://www.scania.com/.
26. Spatial factors affecting the loading efficiency of excavators10.1016/j.autcon.2014.08.002 ↗
27. Jongluxmanee, J., Kohei, O., and Yamakita, M. (2019, January 9–12). Iterative learning control for soil loading operation of excavator. Proceedings of the 2019 12th Asian Control Conference (ASCC), Kitakyushu, Japan.