Integrated navigation describes the combination of two or more navigation sensors to achieve better results than using a single sensor. At the Institute of Geodesy, this is done using Bayesian filters (Kalman and particle filters) and Bayesian networks (Factor Graph Optimization). There are multiple reasons for sensor integration:

• The estimation of all required parameters (e.g.: GNSS receivers cannot provide (all) attitude parameters).
• The improvement of the accuracy, reliability and integrity compared to the results of a single sensor.
• The increase of the update frequency of the solution.

The most common combination is the integration of an IMU (Inertial Measurement Unit) and a GNSS (Global Navigation Satellite System) receiver because of their complementary properties. The optimal filtering of the different sensors includes a dynamic model (model of the expected movement of the vehicle) and a time-dependent noise. This optimal filter is implemented in several research projects as Kalman filter or particle filter. More recently, a focus has been put on Factor Graph Optimization. At the Institute of Geodesy, the following sensors are combined in various ways:

• GNSS
• Inertial Sensors (strapdown approach and pedestrian navigation approaches)
• LiDAR (Light Detection and Ranging)
• Cameras and stereo cameras
• Magnetometers
• Barometer
• Odometry (e.g. directly from the car via CAN bus)
• WiFi data
• BLE data (Bluetooth Low Energy)

Related Projects: NIKE MATE, CONCLUSION, SURUx2, ROBO-MOLE, ANDREA, ANTON