Results 271 to 280 of about 3,103,680 (318)

Generalized Multi-sensor Planning

2006
Vision systems for various tasks are increasingly being deployed. Although significant effort has gone into improving the algorithms for such tasks, there has been relatively little work on determining optimal sensor configurations. This paper addresses this need.
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Indoor geolocation on multi-sensor smartphones

Proceeding of the 11th annual international conference on Mobile systems, applications, and services, 2013
In this demo, we present an efficient hybrid indoor positioning solution that uses multi-sensory location-oriented observations, including WiFi, accelerometer, gyroscope and digital compass data, that are widely available on Android smartphones.
Li, C. -L, Laoudias, C., Larkou, G., Tsai, Y. -K, Zeinalipour-Yazti, D., Panayiotou, Christos G., Li, C. -L, Laoudias, C., Larkou, G., Tsai, Y. -K, Zeinalipour-Yazti, D., Panayiotou, Christos G.   +11 more
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Multi sensor based indoor positioning

2017 International Conference on Computer Science and Engineering (UBMK), 2017
Decreasing the effectiveness of GPS technology in indoor environments has necessitated the use of different and cheaper technologies in these environments. Today's indoor positioning systems tend to be developed using wireless medium technologies such as RFID, BLE, WLAN, Geo-Magnetism, UWB and mobile device technology.
GÜZEL, METEHAN, Ozdemir, Suat, Dervisoglu, Isamail, Uyar, Celalettin   +3 more
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Multi-sensor robot assembly station

Robotics, 1986
Abstract A sensor-controlled robot assembly and inspection system is described. The station is equipped with vision for the inspection of parts and the measurement of position and orientation, and with force sensing for assistance in assembly tasks. The station is controlled by a multiprocessor 68 000 VME system.
Frans C. A. Groen, Erwin R. Komen, M. A. C. Vreeburg, T. P. H. Warmerdam   +3 more
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Multi-sensor Fusion

2014
In the previous chapters, we have discussed issues concerning hardware, communication and network topologies for the practical deployment of Body Sensor Networks (BSNs). The pursuit of low power miniaturised distributed sensing under a patient’s natural physiological conditions has also imposed significant technical challenges on integrating ...
Guang-Zhong Yang, Javier Andreu-Perez, Xiaopeng Hu, Surapa Thiemjarus   +3 more
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Multi sensor 3D indoor localisation

2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2015
We present an indoor localisation system that integrates different sensor modalities, namely Wi-Fi, barometer, iBeacons, step-detection and turn-detection for localisation of pedestrians within buildings over multiple floors. To model the pedestrian's movement, which is constrained by walls and other obstacles, we propose a state transition based upon ...
Frank Ebner, Toni Fetzer, Frank Deinzer, Lukas Köping, Marcin Grzegorzek   +4 more
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Study of multi-sensor fusion for localization

2019 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), 2019
This article covers implementation and testing of sensor data fusion for robot localization. System is capable of finding robot in known environment using LIDAR, odometry and RFID tags. The localization algorithm is based on the particle filter implemented in NVIDIA CUDA. It fuses 3D LIDAR, odometry and RFID data.
Michal Pelka, Karol Majek, Jakub Ratajczak, Janusz Bedkowski, Andrzej Maslowski   +4 more
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Multi-sensor gradual change detection

2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2015
We develop a mixture procedure to monitor parallel streams of data for a change-point that causes gradual change of a subset of data streams. We model the gradual change as a change in the trends of the affected data streams. Observations are assumed initially to be independent standard normal random variables with zero mean.
Yang Cao 0013, Yao Xie 0002
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An Estimator for Multi-Sensor Data Fusion

2006 IEEE International Conference on Systems, Man and Cybernetics, 2006
In this paper, we examine binary hypothesis testing and parameter estimation problem in a sensor network. We address the problem of detection and also the estimation of the underlying parameter at the fusion center by optimally combining the test statistics sent by different sensors.
Chandrashekhara Thejaswi P. S., S. Viswanath Ganapathy, Ranjeet Kumar Patro, Manik Raina, Subhas Kumar Ghosh   +4 more
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Multi-sensor Integration

2018
Low-cost IMU sensors typically show significant amounts of drift and offset. To analyze data from such sensors, additional information is required to compensate for those artefacts. Two main sensor fusion approaches have been proposed: stochastic filtering, often implemented in the form of an extended Kalman filter.
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