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Efficient 3D visual perception for robotic rock breaking

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Standard

Efficient 3D visual perception for robotic rock breaking. / Niu, Longchuan; Chen, Ke; Jia, Kui; Mattila, Jouni.

2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019. IEEE, 2019. s. 1124-1130 (IEEE International Conference on Automation Science and Engineering).

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Harvard

Niu, L, Chen, K, Jia, K & Mattila, J 2019, Efficient 3D visual perception for robotic rock breaking. julkaisussa 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019. IEEE International Conference on Automation Science and Engineering, IEEE, Sivut 1124-1130, Vancouver, Kanada, 22/08/19. https://doi.org/10.1109/COASE.2019.8842859

APA

Niu, L., Chen, K., Jia, K., & Mattila, J. (2019). Efficient 3D visual perception for robotic rock breaking. teoksessa 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019 (Sivut 1124-1130). (IEEE International Conference on Automation Science and Engineering). IEEE. https://doi.org/10.1109/COASE.2019.8842859

Vancouver

Niu L, Chen K, Jia K, Mattila J. Efficient 3D visual perception for robotic rock breaking. julkaisussa 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019. IEEE. 2019. s. 1124-1130. (IEEE International Conference on Automation Science and Engineering). https://doi.org/10.1109/COASE.2019.8842859

Author

Niu, Longchuan ; Chen, Ke ; Jia, Kui ; Mattila, Jouni. / Efficient 3D visual perception for robotic rock breaking. 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019. IEEE, 2019. Sivut 1124-1130 (IEEE International Conference on Automation Science and Engineering).

Bibtex - Lataa

@inproceedings{ef06508a93174620816ac999d62ffdbc,
title = "Efficient 3D visual perception for robotic rock breaking",
abstract = "In recent years, underground mining automation (e.g., the heavy-duty robots carrying rock breaker tools for secondary breaking) has drawn substantial interest. This breaking process is needed only when over-sized rocks threaten to jam the mine material flow. In the worst case, a pile of overlapped rocks can get stuck on top of a crusher's grate plate. For a human operator, it is relatively easy to make the decisions about the rock locations in the pile and the order of rocks to be crushed. In an autonomous operation, a robust and fast visual perception system is needed for executing robot motion commands. In this paper, we propose a pipeline for fast detection and pose estimation of individual rocks in cluttered scenes. We employ the state-of-art YOLOv3 as a 2D detector to perform 3D reconstruction from point cloud for detected rocks in 2D regions using our proposed novel method, and finally estimating the rock centroid positions and the normal-to-surface vectors based on the predicted point cloud. The detected centroids in the scene are ordered according to the depth of rock surface to the camera, which provides the breaking sequence of the rocks. During the system evaluation in the real rock breaking experiments, we have collected a new dataset with 4780 images having from 1 to 12 rocks on a grate plate. The proposed pipeline achieves 97.47{\%} precision on overall detection with a real-time speed around 15Hz.",
author = "Longchuan Niu and Ke Chen and Kui Jia and Jouni Mattila",
note = "EXT={"}Chen, Ke{"} jufoid=73680",
year = "2019",
month = "8",
day = "1",
doi = "10.1109/COASE.2019.8842859",
language = "English",
series = "IEEE International Conference on Automation Science and Engineering",
publisher = "IEEE",
pages = "1124--1130",
booktitle = "2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019",

}

RIS (suitable for import to EndNote) - Lataa

TY - GEN

T1 - Efficient 3D visual perception for robotic rock breaking

AU - Niu, Longchuan

AU - Chen, Ke

AU - Jia, Kui

AU - Mattila, Jouni

N1 - EXT="Chen, Ke" jufoid=73680

PY - 2019/8/1

Y1 - 2019/8/1

N2 - In recent years, underground mining automation (e.g., the heavy-duty robots carrying rock breaker tools for secondary breaking) has drawn substantial interest. This breaking process is needed only when over-sized rocks threaten to jam the mine material flow. In the worst case, a pile of overlapped rocks can get stuck on top of a crusher's grate plate. For a human operator, it is relatively easy to make the decisions about the rock locations in the pile and the order of rocks to be crushed. In an autonomous operation, a robust and fast visual perception system is needed for executing robot motion commands. In this paper, we propose a pipeline for fast detection and pose estimation of individual rocks in cluttered scenes. We employ the state-of-art YOLOv3 as a 2D detector to perform 3D reconstruction from point cloud for detected rocks in 2D regions using our proposed novel method, and finally estimating the rock centroid positions and the normal-to-surface vectors based on the predicted point cloud. The detected centroids in the scene are ordered according to the depth of rock surface to the camera, which provides the breaking sequence of the rocks. During the system evaluation in the real rock breaking experiments, we have collected a new dataset with 4780 images having from 1 to 12 rocks on a grate plate. The proposed pipeline achieves 97.47% precision on overall detection with a real-time speed around 15Hz.

AB - In recent years, underground mining automation (e.g., the heavy-duty robots carrying rock breaker tools for secondary breaking) has drawn substantial interest. This breaking process is needed only when over-sized rocks threaten to jam the mine material flow. In the worst case, a pile of overlapped rocks can get stuck on top of a crusher's grate plate. For a human operator, it is relatively easy to make the decisions about the rock locations in the pile and the order of rocks to be crushed. In an autonomous operation, a robust and fast visual perception system is needed for executing robot motion commands. In this paper, we propose a pipeline for fast detection and pose estimation of individual rocks in cluttered scenes. We employ the state-of-art YOLOv3 as a 2D detector to perform 3D reconstruction from point cloud for detected rocks in 2D regions using our proposed novel method, and finally estimating the rock centroid positions and the normal-to-surface vectors based on the predicted point cloud. The detected centroids in the scene are ordered according to the depth of rock surface to the camera, which provides the breaking sequence of the rocks. During the system evaluation in the real rock breaking experiments, we have collected a new dataset with 4780 images having from 1 to 12 rocks on a grate plate. The proposed pipeline achieves 97.47% precision on overall detection with a real-time speed around 15Hz.

U2 - 10.1109/COASE.2019.8842859

DO - 10.1109/COASE.2019.8842859

M3 - Conference contribution

T3 - IEEE International Conference on Automation Science and Engineering

SP - 1124

EP - 1130

BT - 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019

PB - IEEE

ER -