Autonomous Robots’ Visual Perception in Underground Terrains using Statistical Region Merging

Isafiade, Omowunmi and Osunmakinde, Isaac and Bagula, Antoine (2013) Autonomous Robots’ Visual Perception in Underground Terrains using Statistical Region Merging, Proceedings of International Conference on Computer Vision and Image Processing (ICCVIP), World Academy of Science (WASET), 29-30 April, Johannesburg, South Africa., Johannesburg, 1041-1048, World Academy of Science (WASET).

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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.

Item Type: Conference paper
Uncontrolled Keywords: Drivable Region Detection, Kinect Sensor, Robots’ Perception, SRM, Underground Terrains.
Subjects: Computing methodologies > Computer graphics > Image manipulation > Image processing
Date Deposited: 13 Jun 2013
Last Modified: 10 Oct 2019 15:33

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