Effective venue image retrieval using robust feature extraction and model constrained matching for mobile robot localization

Abstract

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Floor Covering and Surface Identification for Assistive Mobile Robotic Real-Time Room Localization Application

“…The mouse camera, whose spectral sensitivity sits closely around the 600 nm peak [ 21 ], was more tolerant of the wavelength shift occurring with the fluorescent lighting test than the color sensor, the features obtained from the mouse camera significantly improved the overall correct Bayes-1 classification result 19.2%. There are methods, not considered here, for the removal of unwanted lighting; one such mitigating method, utilizing five different room layouts, reported a 75% correct room image identification when three different lighting conditions were employed [ 29 ].…”

A Taxonomy of Vision Systems for Ground Mobile Robots

“…The detection is carried out from a single location and even from multiple viewpoints. A fleet of GMRs was used for mapping an office-like indoor environment – each robot had its own sensor and all the measurements were fused to create a global single map in [62].…”

“…In [61], environmental information acquired from two sensors is combined and fused by a Bayesian sensor fusion technique based on the probabilistic reliability function of each sensor predefined through experiments for the self-localization of a mobile robot with a monocular camera and a laser-structured light sensor. In [62], the authors describe a system for mobile robot localization in an indoor environment, using concepts like homography and matching borrowed from the context of stereo-and content-based image retrieval techniques. A group of points of interest (POIs) is extracted to represent the image for robust matching in order to deal with variations with respect to viewpoint and camera positions.…”

Making Industrial Robots Smarter with Adaptive Reasoning and Autonomous Thinking for Real-Time Tasks in Dynamic Environments: A Case Study

“…In order to provide robotic systems with autonomous adaptability and smart interaction with the environment [9], new capabilities linked to sensory attributes are required. Although there is a number of potential sensing approaches, including ultrasonic [8] or laser, machine vision [9] is widely used not only in robotics [10] but also in many other applications [11][12], due to its satisfactory performance and/or affordable cost. Therefore, the use of cameras, in conjunction with effective image processing, is a feasible approach to dealing with non-uniform lighting conditions and shadows [13] and other problem requirements such as adaptability and robustness [14].…”