A Complementary Vision Strategy for Autonomous Robots in Underground Terrains using SRM and Entropy Models
Omowunmi, Isafiade, Isaac Osunmakinde and Antoine Bagula (2013) A Complementary Vision Strategy for Autonomous Robots in Underground Terrains using SRM and Entropy Models. International Journal of Advanced Robotic Systems 10(338):1-13.
This work investigates robots’ perception in underground terrains (mines and tunnels) using statistical region merging (SRM) and the entropy models. A probabilistic approach based on the local entropy is employed. The entropy is measured within a fixed window on a stream of mine and tunnel frames to compute features used in the segmentation process, while 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. Furthermore, an investigation is also conducted on a stream of dynamic 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 into 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 evaluations, reveal that a good drivable region can be detected in dynamic underground terrains.
|EPrint Type:||Journal (Paginated)|
|Keywords:||3D kinect Sensors, Entropy, SRM, Underground Terrains, Drivable Region Detection, Autonomous Robots|
|Subjects:||I Computing Methodologies: I.4 IMAGE PROCESSING AND COMPUTER VISION|
|Deposited By:||Bagula, Antoine|
|Deposited On:||14 Febuary 2014|