Extended Kalman filter for improved navigation with fault awareness

Stephen Oonk, Francisco J. Maldonado, Zongke Li, Karl Reichard, Jesse Pentzer

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Article number6974332
Pages (from-to)2681-2686
Number of pages6
JournalConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Issue numberJanuary
StatePublished - 2014
Event2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014 - San Diego, United States
Duration: Oct 5 2014Oct 8 2014

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction


Dive into the research topics of 'Extended Kalman filter for improved navigation with fault awareness'. Together they form a unique fingerprint.

Cite this