Department Publications

Accurate attitude estimation in high dynamic motion scenarios like unmanned aerial vehicle navigation or human motion and magnetically disturbed indoor environments requires compensation of accelerometer disturbances as well as magnetometer disturbances. This paper proposes a novel algorithm for accelerometer disturbance rejection by adaptive estimation of accelerometer measurement covariance matrix online. Magnetic disturbances are generally lasting long compared to accelerometer disturbances which are generally short term in indoor navigation environments. Novel two step Kalman filter update method is proposed to separate attitude correction from heading correction. The magnetometer is used only for heading update such that the attitude estimation is not affected by errors in magnetometer measurement. Performances of the proposed algorithms are shown by using real world sensor data.

Pose tracking for outdoor rovers is generally a complex task which is further complicated in conditions where a Global Positioning System (GPS) signal is denied such as in planetary exploration, underground mines and covered areas. In these conditions the rover's pose needs to be calculated purely based on the rover's current environment observations. However, conventional wheel odometry is not reliable on rough terrain where wheels are prone to slip and the wheels do not have a common plane of motion due to suspension systems. This paper proposes a Fast State Variable Extension (Fast-SVE) method in which 2D state variables (x, y, yaw) are extended to the full 3D state (x, y, z, roll, pitch, yaw) to achieve effective real time 3D pose tracking of the rover. A particle filter implementation incorporating the Fast-SVE method is used to track the 3D pose of the rover with roll and pitch values used for weighting. An …