Talk: IMU Self-calibration state Estimation in Autonomous Vehicles
Title: IMU Calibration and Self-correction Algorithm for Open-source Autonomous Vehicle Controller Abstract: An Inertial Measurement Unit (IMU) that measures the linear acceleration and angular velocity of a robot/vehicle is constrained due to physical limitations such as noise, drifts, misalignments and offsets. In order to minimize the drift in state estimates over time, accurate calibration is required.
The key contribution of this project was the development of a method for continuous real-time calibration and self-correction for an IMU, which quickly adapts to changing parameters. This method was tested with simulated as well as real data. The second contribution of this project was a comparison study of attitude estimation algorithms that fuse data from an accelerometer and magnetometer to estimate a robot’s orientation.
