AN ASSISTANCE SYSTEM FOR BUILDING INTELLIGENT SPACES BASED ON MAPSHARING AMONG A MOBILE ROBOT AND DISTRIBUTED SENSORS

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering , Engineering, Electrical & Electronic

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VOLUME 8 , ISSUE 1 (March 2015) > List of articles

AN ASSISTANCE SYSTEM FOR BUILDING INTELLIGENT SPACES BASED ON MAPSHARING AMONG A MOBILE ROBOT AND DISTRIBUTED SENSORS

Fumitaka Hashikawa * / Kazuyuki Morioka

Keywords : Intelligent Space, Mobile Robot, SLAM.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 8, Issue 1, Pages 1-25, DOI: https://doi.org/10.21307/ijssis-2017-746

License : (CC BY-NC-ND 4.0)

Received Date : 30-September-2014 / Accepted: 01-December-2014 / Published Online: 01-March-2015

ARTICLE

ABSTRACT

An intelligent space is a space constructed with many networked sensors. Humans and robots in the space are extracted and tracked cooperatively by the networked sensors. The intelligent space can achieve position-based supports to humans and robots according to integration of networked sensors. Generally, the networked sensors are distributed and fixed on the structures in the space such as walls, ceilings and etc. In order to track moving objects such as humans and robots in the intelligent space, all networked sensors have to obtain objects positions in the unified world coordinate. In that case, positions and orientations of the networked sensors must be also known in the unified world coordinate system. However, it is time-consuming to measure positions of many sensors in the world coordinate accurately and manually. This study aims to develop a system for supporting estimation of positions and orientations of the networked sensors in the intelligent space. In this paper, a configuration of the proposed system is introduced. The proposed system consists of map building systems of the mobile robot and the distributed sensors. A global map from the robot and local maps from the distributed sensors are compared. Then, the local maps of the distributed sensors are associated with the global map and the positions of the distributed sensors are estimated in the global map. For improvement of map matching, angle differences between maps are evaluated. Some experimental results in an actual environment show that the proposed system achieve sensor position estimation easily.

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REFERENCES

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