Brain-inspired close loop detection

• Main Author: Chithra Srinivasan
• Other Authors: Kwoh Chee Keong
• Format: Final Year Project
• Language: English
• Published: 2014
• Subjects: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
• Online Access: http://hdl.handle.net/10356/59046
• Institution: Nanyang Technological University

Loop closing is the problem of correctly asserting that a robot has returning to a previously visited area. This is an extremely important component of the Simultaneous Localization and Mapping (SLAM) problem (Kin Leong Ho, 2006). The many implementations of loop closure in various SLAM techniques involve the internal map and vehicle estimates to come to a conclusion. However, these implementations are prone to much error. The loop closer mechanism that is being proposed here does not involve any of the metric estimates of the SLAM system, rather it is completely independent. It uses an appearance-based SLAM technique to deal with the problem of loop closure. The vehicle captures the appearance of the local scene with the help of a RGBD sensor like Kinect. The captured scenes are classified using a biologically inspired method which uses the shape based image property that is provided by a hierarchical feed forward model of the visual cortex. The H-MAX algorithm has been used for this purpose. The similarities of all the images through the extracted features are then encoded in a ‘similarity matrix’. These sequences as a last step are then used by a dynamic programming algorithm (Smith-Waterman algorithm) to extract the presence of a closed-loop. The technique is also demonstrated successfully with depth based images.