imu-integration

IMU data fusion for robot localization in ROS 2 involves configuring EKF or UKF nodes to combine /imu/data with odometry. This workflow aligns sensor frames per REP 103 and removes gravity to provide odometry-accurate navigation.

Gravity removal and frame alignment for IMU sensor fusion require configuring static transforms and applying REP 103 standards. This ensures acceleration data accurately reflects robot movement rather than gravitational bias during EKF or UKF processing.

Configuring EKF and UKF for 3D robot navigation using IMU data involves setting orientation, yaw rate, and acceleration handling parameters. Proper calibration and static-transform tuning correct mounting inaccuracies for reliable localization.

Madgwick and complementary filters can be used for IMU orientation estimation within a ROS 2 navigation stack. They process raw IMU data into a coherent orientation estimate before feeding it into EKF or UKF fusion workflows.

IMU sensor fusion drift and inaccurate yaw estimates in ROS 2 often stem from uncalibrated sensors or incorrect static transforms. Proper calibration, mounting alignment, and yaw rate handling configuration are essential to resolve these localization issues.

Publishing static transforms is required to correct IMU mounting offset for EKF fusion. Accurate static-transform configuration aligns the IMU frame with the robot base frame, preventing orientation and localization errors during sensor fusion.