GPSimu

GPSIMU refers to a class of navigation systems or software that fuses Global Positioning System data with inertial measurement unit data to estimate a vehicle’s motion in 3D. The integration leverages the complementary properties of GPS and IMU: GPS provides accurate long-term position and velocity but with limited update rate and occasional outages; an IMU offers high-rate measurements of acceleration and rotation but drifts over time. By combining them, GPSIMU delivers robust, real-time estimates of position, velocity, and attitude.

Typical components and operation: A GPS receiver supplies time-stamped position and velocity; an IMU provides 3-axis accelerations and angular rates; a processor runs a sensor fusion algorithm (often an extended Kalman filter or a complementary filter). The filter maintains a state vector, commonly including 3D position, velocity, orientation (quaternion or Euler angles), and biases for the gyros and accelerometers. The algorithm uses GPS data to correct drift in the IMU and uses IMU data to provide smooth, high-rate estimates between GPS updates.

Architecture and outputs: Systems may be embedded on microcontrollers or run on onboard computers; outputs include position, velocity, attitude, and estimated biases; data are timestamped and often synchronized to a common clock. They are used in robotics, aerial drones, autonomous vehicles, marine navigation, and surveying.

Implementation and ecosystem: There are commercial GPS/IMU units and open-source implementations. Software may be written in C/C++ or Python and can integrate with ROS or other middleware. Common hardware includes UBlox or Septentrio GPS receivers and MEMS IMUs.

Challenges and considerations: Performance depends on GPS availability, multipath, satellite geometry, and accurate time synchronization. Initialization, calibration, and alignment affect accuracy; robust outlier handling is important.