Mobile Robot Self-Localization and Local Map Alignment with a Dempster Shafer Sensor Fusion Algorithm

Pyroelectric infrared (PIR) sensors can detect the presence of human without the need to carry any device, which are widely used for human presence detection in home/office automation systems in order to improve energy efficiency. However, PIR detection is based on the movement of occupants. For occupancy detection, PIR sensors have inherent limitation when occupants remain relatively still. Multisensor fusion technology takes advantage of redundant, complementary, or more timely information from different modal sensors, which is considered an effective approach for solving the uncertainty and unreliability problems of sensing. In this paper, we proposed a simple multimodal sensor fusion algorithm, which is very suitable to be manipulated by the sensor nodes of wireless sensor networks. The inference algorithm was evaluated for the sensor detection accuracy and compared to the multisensor fusion using dynamic Bayesian networks. The experimental results showed that a detection accuracy of 97% in room occupancy can be achieved. The accuracy of occupancy detection is very close to that of the dynamic Bayesian networks.

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Evaluation of Matrix Square Root Operations for UKF within a UAV GPS/INS Sensor Fusion Application

International Journal of Navigation and Observation ◽

10.1155/2011/416828 ◽

2011 ◽

Vol 2011 ◽

pp. 1-11 ◽

Cited By ~ 30

Author(s):

Matthew Rhudy ◽

Yu Gu ◽

Jason Gross ◽

Marcello R. Napolitano

Keyword(s):

Sensor Fusion ◽

Unscented Kalman Filter ◽

Attitude Estimation ◽

Square Root ◽

Fusion Algorithm ◽

Matrix Square Root ◽

Global Positioning ◽

The Matrix ◽

Aerial Vehicle ◽

Diagonalization Method

Using an Unscented Kalman Filter (UKF) as the nonlinear estimator within a Global Positioning System/Inertial Navigation System (GPS/INS) sensor fusion algorithm for attitude estimation, various methods of calculating the matrix square root were discussed and compared. Specifically, the diagonalization method, Schur method, Cholesky method, and five different iterative methods were compared. Additionally, a different method of handling the matrix square root requirement, the square-root UKF (SR-UKF), was evaluated. The different matrix square root calculations were compared based on computational requirements and the sensor fusion attitude estimation performance, which was evaluated using flight data from an Unmanned Aerial Vehicle (UAV). The roll and pitch angle estimates were compared with independently measured values from a high quality mechanical vertical gyroscope. This manuscript represents the first comprehensive analysis of the matrix square root calculations in the context of UKF. From this analysis, it was determined that the best overall matrix square root calculation for UKF applications in terms of performance and execution time is the Cholesky method.

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Low Cost Sensor Data Fusion in Omnidirectional Mobile Robot Feedback System to Improve the Navigation Accuracy

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.607.791 ◽

2014 ◽

Vol 607 ◽

pp. 791-794 ◽

Cited By ~ 2

Author(s):

Wei Kang Tey ◽

Che Fai Yeong ◽

Yip Loon Seow ◽

Eileen Lee Ming Su ◽

Swee Ho Tang

Keyword(s):

Mobile Robot ◽

Sensor Fusion ◽

Low Cost ◽

Feedback System ◽

Sensor Data ◽

Research Setting ◽

Fusion Technique ◽

Omnidirectional Mobile Robot ◽

The Cost ◽

Navigation Accuracy

Omnidirectional mobile robot has gained popularity among researchers. However, omnidirectional mobile robot is rarely been applied in industry field especially in the factory which is relatively more dynamic than normal research setting condition. Hence, it is very important to have a stable yet reliable feedback system to allow a more efficient and better performance controller on the robot. In order to ensure the reliability of the robot, many of the researchers use high cost solution in the feedback of the robot. For example, there are researchers use global camera as feedback. This solution has increases the cost of the robot setup fee to a relatively high amount. The setup system is also hard to modify and lack of flexibility. In this paper, a novel sensor fusion technique is proposed and the result is discussed.

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