Predictive hierarchical human augmented map generation for itinerary perception

• Published in: Electronics letters Vol. 52; no. 16; pp. 1381 - 1383
• Main Authors: Kaur, B, Bhattacharya, J
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 04.08.2016
• Subjects: augmented reality; cartography; Climatology; Electronics; environmental conditions; Gaussian; Gaussian process; Gaussian processes; Image and vision processing and display technology; image sensors; information retrieval; itinerary perception; learning (artificial intelligence); machine learning technique; mobile robots; path planning; Perception; predictive hierarchical human augmented map generation; region‐of‐interest input acquisition; robot vision; service robot; service robots; timing events; traffic density distribution database; Traffic engineering; traffic engineering computing; Traffic flow; traffic related information extraction; vision sensor; Weather; vision sensor; traffic related information extraction; machine learning technique; timing events; region-of-interest input acquisition; information retrieval; augmented reality; robot vision; cartography; traffic engineering computing; mobile robots; path planning; service robot; image sensors; Gaussian process; service robots; traffic density distribution database; Gaussian processes; predictive hierarchical human augmented map generation; learning (artificial intelligence); environmental conditions; itinerary perception
• DOI: 10.1049/el.2016.0397
• ISSN: 0013-5194; 1350-911X

A new class of augmented map application is introduced which can provide detailed knowledge about any area, to a user. This brief particularly focuses on obtaining itinerary perception subject to different environmental conditions. This refers to extraction of traffic related information from an augmented map. The problem is modelled as a machine learning technique where the traffic distribution at different times (including same days, different days and different weather) are observed continuously using a service robot. This data is posed as a Gaussian process for post-estimation. Our system consists of a vision sensor which will acquire the region of interest input, queried to a database of traffic density distributions, learned from the scenes at different points of time. The user interacting with the system will obtain an information pertaining to the region conditioned on environmental and timing events.