@article{(Open Science Index):https://publications.waset.org/pdf/994,
	  title     = {Autonomous Robots- Visual Perception in Underground Terrains Using Statistical Region Merging},
	  author    = {Omowunmi E. Isafiade and  Isaac O. Osunmakinde and  Antoine B. Bagula},
	  country	= {},
	  institution	= {},
	  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.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {7},
	  number    = {4},
	  year      = {2013},
	  pages     = {496 - 503},
	  ee        = {https://publications.waset.org/pdf/994},
	  url   	= {https://publications.waset.org/vol/76},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 76, 2013},
	}