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Biologically Inspired Monocular Vision Based Navigation and Mapping in GPS-Denied Environments
"... This paper presents an in-depth theoretical study of bio-vision inspired feature extrac-tion and depth perception method integrated with vision-based simultaneous localization and mapping (SLAM). We incorporate the key functions of developed visual cortex in several advanced species, including human ..."
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humans, for depth perception and pattern recognition. Our navigation strategy assumes GPS-denied manmade environment consisting of orthog-onal walls, corridors and doors. By exploiting the architectural features of the indoors, we introduce a method for gathering useful landmarks from a monocular camera
An Architecture for Online Semantic Labeling on UGVs
"... We describe an architecture to provide online semantic labeling capabilities to field robots operating in urban environments. At the core of our system is the stacked hierarchical classifier developed by Munoz et al., 1 which classifies regions in monocular color images using models derived from han ..."
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Cited by 1 (1 self)
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We describe an architecture to provide online semantic labeling capabilities to field robots operating in urban environments. At the core of our system is the stacked hierarchical classifier developed by Munoz et al., 1 which classifies regions in monocular color images using models derived from
Markov Random Field based Small Obstacle Discovery over Images
"... Abstract—Small obstacles of the order of 0.5 − 3cms and homogeneous scenes often pose a problem for indoor mobile robots. These obstacles cannot be clearly distinguished even with the state of the art depth sensors or laser range finders using existing vision based algorithms. With the advent of sop ..."
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of sophisticated image processing algorithms like SLIC [1] and LSD [9], it is possible to extract rich information from an image which led us to develop a novel architecture to detect very small obstacles on the floor using a monocular camera. This information is further processed using a Markov Random Field based
