A Kalman Filter Approach to the Fusion of Acceleration, GNSS position and Rotation Sensor Data from Robot Motions

<strong>GNSS data:</strong> Instrument: Javad antenna and Septentrio receiver sampling rate: 100 Hz Bandwidth of loop filter: auto adjust Relative positioning Baseline: ultra short with distance of 5 m files in Rinex format: Rover (moving antenna) and Base (stationary antenna), .20G (GLONASS Navigation data), .20N (GPS Navigation data), .20L (Galileo Navigation data), .20O (Observations) <strong>Accelerometer data:</strong> Instrument: EpiSensor and Centaur Digitizer Sampling rate: 250 Hz Unit: counts unfiltered file: XKUK_centaur-6_1233_20200908_114500.seed <strong>Angular rate data:</strong> Instrument: IMU KvH 1750 (includes accelerometer and rotational sensor) Sampling rate: 250 Hz Unit gyro: rad/s Unit accelerometer: g (gravitational acceleration) file: LOGGING_1750_IMU_1308K004_11_57_25_250.csv <strong>Robot Feedback:</strong> Instrument: KUKA model AGILUS KR 6 R900 sixx Sampling rate: 250 Hz Unit translation: m Unit rotation: degree files: kuka_motion_*.txt, 1-4 are consecutive in time. <strong>Experiments:</strong> T: translations, R: rotations, XL, L, S denote the relative amplitudes 10 experiments: TLRXL, TLRXL, TLRL, TLRL, TLRS, unfinished TLRS, TLRS, TLRS, TSRS, TSRS (Robot feedback (1,2), angular rate, GNSS data) 9 experiments: TLRXL, TLRXL, TLRL, TLRL, TLRS, unfinished TLRS, TLRS, TSRS, TSRS (Robot feedback (3,4), accelerometer data