The Xsens Vision Navigator (XVN) is a versatile tool that combines the information of GNSS (with RTK support), camera, IMU, and other external sensors (e.g., wheel encoders) at all times. This allows it to estimate its position in a variety of scenarios, whether it is in clear sky, GNSS-degraded (i.e., partial coverage due to building or foliage), or GNSS-denied environments.
In situations where the estimated position from the GNSS receivers is degraded, the Xsens Vision Navigator can assess the quality of each GNSS signal and determine whether to ignore a measurement or use it for position or velocity estimates.
During complete GNSS outages, the Xsens Vision Navigator showcases its adaptability. It relies on its visual-inertial odometry estimation, which is aided by wheel odometry estimates or other sensors if available. This approach causes the position estimate to drift over the distance traveled (approximately 1% over 100 meters), but it is important to note that the sensor does not drift over time. This is due to the combination of visual data with the IMU measurements, which effectively constrains the noise and bias growth of the IMU.
Note that in very open areas where the camera might not extract sufficient features for accurate visual-inertial estimation, GNSS signals would be available. Even if RTK corrections are no longer provided, the XVN can use GNSS to constrain the sensor's drift and aid its estimation with position (less accurate) and velocity (more precise) measurements. RTK corrections are crucial for ensuring centimeter-accurate positioning outdoors, but the XVN can operate at the decimeter level without corrections by relying on other techniques.
Furthermore, even in environments with sparse features, the camera can still gather visual cues from the ground, the sky, and far-away objects (e.g., trees or buildings) to assist the position and heading estimate. Even though the quality of the extracted visual features might be diminished, the XVN will promptly adapt its confidence to rely more on other sensors (e.g., IMU and wheel odometry).