Kalman filtering: with real-time applications (2nd ed.)
Kalman filtering: with real-time applications (2nd ed.)
3D Position Tracking in Challenging Terrain
International Journal of Robotics Research
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This paper focuses on the design and test results of an adaptive variation of Kalman filter (KF) estimator based on fusing data from Inertial Measurement Unit (IMU) and two Real Time Kinematic (RTK) Global Positioning Systems (GPS) for driftless 3-D attitude determination and robust position estimation of mobile robots. GPS devices are notorious for their measurement errors vary from one point to the next. Therefore in order to improve the quality of the attitude estimates, the covariance matrix of measurement noise is estimated in real time upon information obtained from the differential GPS measurements, so that the KF filter continually is "tuned" as well as possible. No a priori knowledge on the direction cosines of the gravity vector in the inertial frame is required as these parameters can be also identified by the KF, relieving any need for calibration. Next, taking advantage of the redundant GPS measurements, a weight least-squares estimator is derived to weight the GPS measurement with the "good" data more heavily than the one with "poor" data in the estimation process leading to a robust position estimation. Test results are presented showing the performance of the integrated IMU and two GPS to estimate the attitude and location of a mobile robot moving across uneven terrain.