A Complementary Vision Strategy for Autonomous Robots in Underground Terrains using SRM and Entropy Models

• Main Authors: Omowunmi Isafiade, Isaac Osunmakinde, Antoine Bagula
• Format: Article
• Language: English
• Published: SAGE Publishing 2013-09-01
• Series: International Journal of Advanced Robotic Systems
• Online Access: https://doi.org/10.5772/56758

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 × 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.