Autonomous Robots- Visual Perception in Underground Terrains Using Statistical Region Merging
Robots- visual perception is a field that is gaining
increasing attention from researchers. This is partly due to emerging
trends in the commercial availability of 3D scanning systems or
devices that produce a high information accuracy level for a variety of
applications. In the history of mining, the mortality rate of mine workers
has been alarming and robots exhibit a great deal of potentials to
tackle safety issues in mines. However, an effective vision system
is crucial to safe autonomous navigation in underground terrains.
This work investigates robots- perception in underground terrains
(mines and tunnels) using statistical region merging (SRM) model.
SRM reconstructs the main structural components of an imagery
by a simple but effective statistical analysis. An investigation is
conducted on different regions of the mine, such as the shaft, stope
and gallery, using publicly available mine frames, with a stream of
locally captured mine images. An investigation is also conducted on a
stream of underground tunnel image frames, using the XBOX Kinect
3D sensors. The Kinect sensors produce streams of red, green and
blue (RGB) and depth images of 640 x 480 resolution at 30 frames per
second. Integrating the depth information to drivability gives a strong
cue to the analysis, which detects 3D results augmenting drivable and
non-drivable regions in 2D. The results of the 2D and 3D experiment
with different terrains, mines and tunnels, together with the qualitative
and quantitative evaluation, reveal that a good drivable region can be
detected in dynamic underground terrains.
[1] S. Chen and Y. Li and N. Ming, Active Vision in Robotic Systems: A
Survey of Recent Developments, The International Journal of Robotics
Research, AID: 0278364911410755, Vol. 30, No. 11 ,pp. 1343 1377,
September, 2011.
[2] A. Damarupurshad, African (Mining-Related) Government Departments,
Mineral/Mining-Related Organisations and Mining Companies with Interests
in Africa, Journal of Minerals and Energy (Republic of South
Africa), Vol. D13, No. 1 ,pp. 1-58, June, 2004.
[3] PricewaterhouseCoopers. SA Mine: Review of Trends in the South African
Mining Industry, SA Mine, Vol. 6 No. 1, pp. 1-48, November 2011.
[4] C.J.H. Hartnady, South Africa-s Gold Production and Reserves, South
African Journal of Science, Vol. 105, No. 1 ,pp. 328-330, September,
2009.
[5] M. A. Hermanus, Occupational Health and Safety in Mining- Status, New
Developments and Concerns, Journal of the Southern African Institute of
Mining and Metallurgy, Vol. 107, No. 1 ,pp. 531-538, May, 2007.
[6] J. J. Green and K. Hlophe and J. Dickens and R. Teleka and
M. Price, Mining Robotics Sensors, International Journal of Engineering
and Advanced Technology (IJEAT), Vol. 1, No. 4 ,pp. 8-15, April,
2012.
[7] J. Benjamin and Y. Papelis and R. Pillat and G. Stein and D. Harper,A
Practical Approach to Robotic Design for the DARPA Urban Challenge,
International Journal of Field Robotics, Vol. 25, No. 8 ,pp. 528-566,
July, 2008.
[8] T. Joaquin and N. Patricio, A New Approach to Visual-Based Sensory
System for Navigation into Orange Groves, Journal of MDPI Open Access
Sensors, Vol. 11, pp. 4086-4103, April 2011.
[9] H. Derek and A. Alexei and H. Martial. Recovering Surface Layout from
an Image, International Journal of Computer Vision, Vol. 75, pp.151-172,
2007.
[10] S. Scheding and G. Dissanayake and E. Nebot and H. Durrant-Whyte,
An Experiment in Autonomous Navigation of an Underground Mining
Vehicle, IEEE Transactions on Robotics and Automation, Vol. 15, No. 1
,pp. 85-95, 1999.
[11] K. Khoshelham and S. Oude Elberink, Accuracy and Resolution of
Kinect Depth Data for Indoor Mapping Applications, Journal of MDPI
Open Access Sensors, Vol. 12, No. 1 ,pp. 1437-1454, February, 2012.
[12] T. Fawcett, An Introduction to ROC analysis, Pattern Recognition
Letters, Vol. 27, No. 1, pp. 861-874, December, 2005.
[13] S. Battiato and G.M. Farinella and G. Puglisi. SVG Vectorization by
Statistical Region Merging. Proceedings of 4th Conference Eurographics
Italian Chapter, pages 1-7, July, 2006.
[14] R. Nock and F. Nielsen, Statistical Region Merging, IEEE Transactions
on Pattern Analysis and Machine Intelligence, Vol. 26, No. 11 ,pp. 1-7,
November, 2004.
[15] F. Calderero and F. Marques, Region Merging Techniques Using Information
Theory Statistical Measures, IEEE Transactions on Image
Processing, Vol. 19, No. 16 ,pp. 1567-1586, June, 2010.
[16] M.E. Celebi and H. A. Kingravi and J. Lee and W. Stoecker and J.
M. Malters and H. Iyatomi and Y.A. Aslandogan and R. Moss and
A.A. Marghoob, Fast and Accurate Border Detection in Dermoscopy
Images Using Statistical Region Merging, TexasWorkforce Commission
James A. Schlipmann Melanoma Cancer Foundation and NIH (SBIR
2R44 CA-101639-02A2), No. 16 ,pp. 1-10, 2006.
[17] H. Li and H. Gu and Y. Han and J. Yang, An Efficient Multiscale
SRMMHR (Statistical Region Merging and Minimum Heterogeneity Rule)
Segmentation Method for High Resolution Remote Sensing Imagery, IEEE
Journal of Selected Topics in Applied Earth Observations and Remote
Sensing, Vol. 2, No. 2 ,pp. 67-73, June, 2009.
[18] Bretterk. Irominers, Available online: http://brettberk.com/wpcontent/
uploads/2009/05/death-valley-looking-inside-mine-shaft.jpg
(accessed on 10 May 2012).
[19] C. Hsu and F. Lian and C. Huang and Y. Chang. Detecting Drivable
Space in Traffic Scene Understanding. Proceedings of the IEEE International
Conference on Systems Science and Engineering, pp 79-84, June,
2012.
[20] S. Zhou and J. Gong and G. Xiong and H. Chen and K. Iagnemma.
Road Detection using Support Vector Machine Based on Online Learning
and Evaluation. In IEEE Intelligent Vehicles Symposium, pp 256-262.,
San Diego USA, 2010.
[21] W. Jian and J. zhong and S. Yu-Ting. Unstructured Road Detection using
Hybrid Features. Proceedings of the Eighth International Conference
on Machine Learning and Cybernetics,, pp. 482-486., July 2009.
[22] S.R. Teleka and J. J. Green and S. Brink and J. Sheer and K. Hlophe.
The Automation of the ÔÇÿMaking Safe- Process in South African Hard-Rock
Underground Mines, International Journal of Engineering and Advanced
Technology (IJEAT) Vol. 1, pp. 1-7., April 2012.
[1] S. Chen and Y. Li and N. Ming, Active Vision in Robotic Systems: A
Survey of Recent Developments, The International Journal of Robotics
Research, AID: 0278364911410755, Vol. 30, No. 11 ,pp. 1343 1377,
September, 2011.
[2] A. Damarupurshad, African (Mining-Related) Government Departments,
Mineral/Mining-Related Organisations and Mining Companies with Interests
in Africa, Journal of Minerals and Energy (Republic of South
Africa), Vol. D13, No. 1 ,pp. 1-58, June, 2004.
[3] PricewaterhouseCoopers. SA Mine: Review of Trends in the South African
Mining Industry, SA Mine, Vol. 6 No. 1, pp. 1-48, November 2011.
[4] C.J.H. Hartnady, South Africa-s Gold Production and Reserves, South
African Journal of Science, Vol. 105, No. 1 ,pp. 328-330, September,
2009.
[5] M. A. Hermanus, Occupational Health and Safety in Mining- Status, New
Developments and Concerns, Journal of the Southern African Institute of
Mining and Metallurgy, Vol. 107, No. 1 ,pp. 531-538, May, 2007.
[6] J. J. Green and K. Hlophe and J. Dickens and R. Teleka and
M. Price, Mining Robotics Sensors, International Journal of Engineering
and Advanced Technology (IJEAT), Vol. 1, No. 4 ,pp. 8-15, April,
2012.
[7] J. Benjamin and Y. Papelis and R. Pillat and G. Stein and D. Harper,A
Practical Approach to Robotic Design for the DARPA Urban Challenge,
International Journal of Field Robotics, Vol. 25, No. 8 ,pp. 528-566,
July, 2008.
[8] T. Joaquin and N. Patricio, A New Approach to Visual-Based Sensory
System for Navigation into Orange Groves, Journal of MDPI Open Access
Sensors, Vol. 11, pp. 4086-4103, April 2011.
[9] H. Derek and A. Alexei and H. Martial. Recovering Surface Layout from
an Image, International Journal of Computer Vision, Vol. 75, pp.151-172,
2007.
[10] S. Scheding and G. Dissanayake and E. Nebot and H. Durrant-Whyte,
An Experiment in Autonomous Navigation of an Underground Mining
Vehicle, IEEE Transactions on Robotics and Automation, Vol. 15, No. 1
,pp. 85-95, 1999.
[11] K. Khoshelham and S. Oude Elberink, Accuracy and Resolution of
Kinect Depth Data for Indoor Mapping Applications, Journal of MDPI
Open Access Sensors, Vol. 12, No. 1 ,pp. 1437-1454, February, 2012.
[12] T. Fawcett, An Introduction to ROC analysis, Pattern Recognition
Letters, Vol. 27, No. 1, pp. 861-874, December, 2005.
[13] S. Battiato and G.M. Farinella and G. Puglisi. SVG Vectorization by
Statistical Region Merging. Proceedings of 4th Conference Eurographics
Italian Chapter, pages 1-7, July, 2006.
[14] R. Nock and F. Nielsen, Statistical Region Merging, IEEE Transactions
on Pattern Analysis and Machine Intelligence, Vol. 26, No. 11 ,pp. 1-7,
November, 2004.
[15] F. Calderero and F. Marques, Region Merging Techniques Using Information
Theory Statistical Measures, IEEE Transactions on Image
Processing, Vol. 19, No. 16 ,pp. 1567-1586, June, 2010.
[16] M.E. Celebi and H. A. Kingravi and J. Lee and W. Stoecker and J.
M. Malters and H. Iyatomi and Y.A. Aslandogan and R. Moss and
A.A. Marghoob, Fast and Accurate Border Detection in Dermoscopy
Images Using Statistical Region Merging, TexasWorkforce Commission
James A. Schlipmann Melanoma Cancer Foundation and NIH (SBIR
2R44 CA-101639-02A2), No. 16 ,pp. 1-10, 2006.
[17] H. Li and H. Gu and Y. Han and J. Yang, An Efficient Multiscale
SRMMHR (Statistical Region Merging and Minimum Heterogeneity Rule)
Segmentation Method for High Resolution Remote Sensing Imagery, IEEE
Journal of Selected Topics in Applied Earth Observations and Remote
Sensing, Vol. 2, No. 2 ,pp. 67-73, June, 2009.
[18] Bretterk. Irominers, Available online: http://brettberk.com/wpcontent/
uploads/2009/05/death-valley-looking-inside-mine-shaft.jpg
(accessed on 10 May 2012).
[19] C. Hsu and F. Lian and C. Huang and Y. Chang. Detecting Drivable
Space in Traffic Scene Understanding. Proceedings of the IEEE International
Conference on Systems Science and Engineering, pp 79-84, June,
2012.
[20] S. Zhou and J. Gong and G. Xiong and H. Chen and K. Iagnemma.
Road Detection using Support Vector Machine Based on Online Learning
and Evaluation. In IEEE Intelligent Vehicles Symposium, pp 256-262.,
San Diego USA, 2010.
[21] W. Jian and J. zhong and S. Yu-Ting. Unstructured Road Detection using
Hybrid Features. Proceedings of the Eighth International Conference
on Machine Learning and Cybernetics,, pp. 482-486., July 2009.
[22] S.R. Teleka and J. J. Green and S. Brink and J. Sheer and K. Hlophe.
The Automation of the ÔÇÿMaking Safe- Process in South African Hard-Rock
Underground Mines, International Journal of Engineering and Advanced
Technology (IJEAT) Vol. 1, pp. 1-7., April 2012.
@article{"International Journal of Information, Control and Computer Sciences:50139", author = "Omowunmi E. Isafiade and Isaac O. Osunmakinde and Antoine B. Bagula", title = "Autonomous Robots- Visual Perception in Underground Terrains Using Statistical Region Merging", abstract = "Robots- visual perception is a field that is gaining
increasing attention from researchers. This is partly due to emerging
trends in the commercial availability of 3D scanning systems or
devices that produce a high information accuracy level for a variety of
applications. In the history of mining, the mortality rate of mine workers
has been alarming and robots exhibit a great deal of potentials to
tackle safety issues in mines. However, an effective vision system
is crucial to safe autonomous navigation in underground terrains.
This work investigates robots- perception in underground terrains
(mines and tunnels) using statistical region merging (SRM) model.
SRM reconstructs the main structural components of an imagery
by a simple but effective statistical analysis. An investigation is
conducted on different regions of the mine, such as the shaft, stope
and gallery, using publicly available mine frames, with a stream of
locally captured mine images. An investigation is also conducted on a
stream of underground tunnel image frames, using the XBOX Kinect
3D sensors. The Kinect sensors produce streams of red, green and
blue (RGB) and depth images of 640 x 480 resolution at 30 frames per
second. Integrating the depth information to drivability gives a strong
cue to the analysis, which detects 3D results augmenting drivable and
non-drivable regions in 2D. The results of the 2D and 3D experiment
with different terrains, mines and tunnels, together with the qualitative
and quantitative evaluation, reveal that a good drivable region can be
detected in dynamic underground terrains.", keywords = "Drivable Region Detection, Kinect Sensor, Robots'
Perception, SRM, Underground Terrains.", volume = "7", number = "4", pages = "444-8", }
