ekf-sensor-fusion

Sensor fusion for ROS 2 localization combines odometry, IMU, and pose data into a single smooth state estimate by configuring a 15-dimensional EKF with explicit frames, covariances, and timing parameters.

Configuring the robot_localization ekf_filter_node involves setting odom0, imu0, pose0, and twist0 inputs alongside corresponding fuse masks to properly integrate multiple sensor sources into one accurate estimate.

Yes, 2D mode constrains Z, roll, and pitch dimensions during sensor fusion, making it directly applicable for ground robots by limiting the 15-dimensional state estimate to planar navigation.

Setting up frames for ROS 2 EKF requires configuring map, odom, and base_link framing options alongside sensor timeout and transform handling to ensure reliable mobile robot localization.

Jumpy EKF output during GPS and odometry fusion often stems from improperly configured sensor covariances, timing parameters, or mismatched frame transforms within the robot_localization state estimation pipeline.