Clustering Analysis for Secondary Breaking Using a Low-Cost Time-of-Flight Camera

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Clustering Analysis for Secondary Breaking Using a Low-Cost Time-of-Flight Camera. / Niu, Longchuan ; Aref, Mohammad M.; Mattila, Jouni.

2018 Ninth International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, 2018. p. 318-324.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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Niu, L , Aref, MM & Mattila, J 2018, Clustering Analysis for Secondary Breaking Using a Low-Cost Time-of-Flight Camera. in 2018 Ninth International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, pp. 318-324, International Conference on Intelligent Control and Information Processing, 1/01/00. https://doi.org/10.1109/ICICIP.2018.8606682

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@inproceedings{c6765b7ba9824cac99b44f1031e7c5d0,

title = "Clustering Analysis for Secondary Breaking Using a Low-Cost Time-of-Flight Camera",

abstract = "The integration of robust perception in a heavy-duty manipulation control system is an enabler for autonomous mining. This paper aims to analyze performance and robustness of clustering methods for object recognition during the secondary breaking stage of mining. Secondary breaking refers to breaking over-sized rocks into smaller pieces for the purpose of grinding and extraction of valuable ores and minerals. Therefore, recognition of rock pieces is the detection of unstructured targets within a structured environment. The clustering methods are experimentally evaluated by several sets of scenes of point clouds as outputs of a Time-of-Flight camera (ToF). The challenges of rock detection from sparse 3D point cloud data are addressed. In outdoor conditions, ToFs generally provide coarse but robust output in short sample times. Therefore, some clustering methods can be prone to numerical and statistical errors. This paper highlights the weaknesses and strengths of three methods for the secondary breaking application. We propose an algorithmic method for exploiting the existing clustering and segmentation methods efficiently in the detection loop to determine a suitable contact point and approaching angle for a hydraulic jack hammer. The results verify effectiveness of the proposed approach for scattered outputs of low-cost ToFs.",

keywords = "Three-dimensional displays, Rocks, Cameras, Clustering methods, Clustering algorithms, Gaussian mixture model, Range sensing, time-of-flight camera, automatic extraction, 3D point clouds, clustering.",

author = "Longchuan Niu and Aref, {Mohammad M.} and Jouni Mattila",

year = "2018",

month = "11",

doi = "10.1109/ICICIP.2018.8606682",

language = "English",

isbn = "978-1-5386-5861-1",

pages = "318--324",

booktitle = "2018 Ninth International Conference on Intelligent Control and Information Processing (ICICIP)",

publisher = "IEEE",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Clustering Analysis for Secondary Breaking Using a Low-Cost Time-of-Flight Camera

AU - Niu, Longchuan

AU - Aref, Mohammad M.

AU - Mattila, Jouni

PY - 2018/11

Y1 - 2018/11

N2 - The integration of robust perception in a heavy-duty manipulation control system is an enabler for autonomous mining. This paper aims to analyze performance and robustness of clustering methods for object recognition during the secondary breaking stage of mining. Secondary breaking refers to breaking over-sized rocks into smaller pieces for the purpose of grinding and extraction of valuable ores and minerals. Therefore, recognition of rock pieces is the detection of unstructured targets within a structured environment. The clustering methods are experimentally evaluated by several sets of scenes of point clouds as outputs of a Time-of-Flight camera (ToF). The challenges of rock detection from sparse 3D point cloud data are addressed. In outdoor conditions, ToFs generally provide coarse but robust output in short sample times. Therefore, some clustering methods can be prone to numerical and statistical errors. This paper highlights the weaknesses and strengths of three methods for the secondary breaking application. We propose an algorithmic method for exploiting the existing clustering and segmentation methods efficiently in the detection loop to determine a suitable contact point and approaching angle for a hydraulic jack hammer. The results verify effectiveness of the proposed approach for scattered outputs of low-cost ToFs.

AB - The integration of robust perception in a heavy-duty manipulation control system is an enabler for autonomous mining. This paper aims to analyze performance and robustness of clustering methods for object recognition during the secondary breaking stage of mining. Secondary breaking refers to breaking over-sized rocks into smaller pieces for the purpose of grinding and extraction of valuable ores and minerals. Therefore, recognition of rock pieces is the detection of unstructured targets within a structured environment. The clustering methods are experimentally evaluated by several sets of scenes of point clouds as outputs of a Time-of-Flight camera (ToF). The challenges of rock detection from sparse 3D point cloud data are addressed. In outdoor conditions, ToFs generally provide coarse but robust output in short sample times. Therefore, some clustering methods can be prone to numerical and statistical errors. This paper highlights the weaknesses and strengths of three methods for the secondary breaking application. We propose an algorithmic method for exploiting the existing clustering and segmentation methods efficiently in the detection loop to determine a suitable contact point and approaching angle for a hydraulic jack hammer. The results verify effectiveness of the proposed approach for scattered outputs of low-cost ToFs.

KW - Three-dimensional displays

KW - Rocks

KW - Cameras

KW - Clustering methods

KW - Clustering algorithms

KW - Gaussian mixture model

KW - Range sensing

KW - time-of-flight camera

KW - automatic extraction

KW - 3D point clouds

KW - clustering.

U2 - 10.1109/ICICIP.2018.8606682

DO - 10.1109/ICICIP.2018.8606682

M3 - Conference contribution

SN - 978-1-5386-5861-1

SP - 318

EP - 324

BT - 2018 Ninth International Conference on Intelligent Control and Information Processing (ICICIP)

PB - IEEE

ER -

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