Paper Info
Title
Extended kalman filter for improved navigation with fault awareness
Abstract
Most unmanned mobile robotic platforms contain multiple sensors that can be leveraged to measure vehicle motion states, where there often exists redundancies among the different sensor types. Kalman filter based sensor fusion between inertial navigation sensors, GPS readings, encoders, etc. is a very popular approach in the literature to improve the accuracy of navigation readings. However, such redundancies can also be exploited for simultaneously conducting fault detection and identification of the sensors and the robot. This paper presents theory and results for an Extended Kalman Filter (EKF) approach fusing IMU/INS readings with GPS and/or visual odometry (VO) data to diagnose faults in wheel odometry readings (encoders). A key advantage is that the approach works for detecting faults, even when relatively low grade and inexpensive sensors are installed in the vehicle.
Year
DOI
Venue
2014
10.1109/SMC.2014.6974332
Systems, Man and Cybernetics
Keywords
Field
DocType
Global Positioning System,Kalman filters,distance measurement,fault diagnosis,mobile robots,motion control,nonlinear filters,path planning,remotely operated vehicles,sensor fusion,EKF approach,GPS,IMU/INS readings,VO data,encoders,extended Kalman filter,fault awareness,fault detection,fault identification,multiple sensors,navigation,sensor types,unmanned mobile robotic platforms,vehicle motion states,visual odometry,wheel odometry readings,Kalman filtering,fault detection,health monitoring,inertial measurements,mobile robots,navigation,sensor fusion
Computer vision,Extended Kalman filter,Control theory,Computer science,Sensor fusion,Artificial intelligence
Conference
ISSN
Citations 
PageRank 
1062-922X
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Stephen Oonk196.55
Francisco J. Maldonado2119.48
Zongke Li300.34
Karl Reichard4123.10
Jesse Pentzer5122.76