🔵  GNSS can be affected by multipath, signal blockage, or complete outages. IMUs drift over time. Cameras depend on lighting conditions. LiDAR and radar each have their own strengths—and limitations.

👉  The real value doesn’t come from a single sensor. ➡️ It comes from combining them intelligently.

🟠  By continuously fusing data from GNSS, IMU, LiDAR, cameras, odometry, and other sensors, robust positioning can be maintained—even in environments where individual sensors struggle.

💡  At ANavS, this principle is at the core of our technology. Our sensor fusion algorithms combine complementary information in real time to provide reliable, continuous, and resilient positioning for automated driving, robotics, and other safety-critical applications.

🟠  Because robust positioning isn’t built on one perfect sensor—it’s built on the intelligence of many.

Der Beitrag Behind the algorithm – How Sensorfusion works! erschien zuerst auf ANavS®.

🔵 In urban canyons, tunnels, forests, or under dense foliage, GNSS signals can be reflected, weakened, or completely blocked. Even when a position is still available, its accuracy may already be compromised.

🟠 This is where SENSOR FUSION makes the difference.

🟠 By intelligently combining data from GNSS, IMU, LiDAR, radar, cameras, and additional sensors, reliable positioning can be maintained—even in challenging environments where GNSS alone reaches its limits.

💡  At ANavS, our AI-ROX technology leverages resilient sensor fusion to deliver robust and continuous positioning, enabling safer testing, autonomous driving, and mission-critical applications.

👉  Because reliable positioning isn’t about one perfect sensor—it’s about combining the strengths of many.

Der Beitrag Why GNSS alone is not enough! erschien zuerst auf ANavS®.

🔵 The problem:
A receiver may calculate an incorrect position without realizing that the navigation data has been manipulated. For autonomous systems or critical infrastructure, this can have serious consequences.

🟠 The solution:
OSNMA (Open Service Navigation Message Authentication) enables the cryptographic authentication of Galileo’s navigation messages. This allows the receiver to verify that the data is genuine and unaltered.

💡 The Benefit
OSNMA does not make GNSS more accurate, but it does make it more trustworthy—an important foundation for secure and robust navigation. Combined with sensor fusion and integrity monitoring, it provides additional reliability.

🟠 At ANavS, we offer solutions for secure and robust navigation—through the intelligent combination of GNSS, sensor fusion, and integrity monitoring.

Der Beitrag Can You Trust Your GNSS Signal? erschien zuerst auf ANavS®.

👉 GNSS disruptions caused by jamming, spoofing, or meaconing are on the rise worldwide. Manipulated or disrupted position data can lead to wrong decisions with far-reaching consequences.

The real challenge today is no longer calculating a position—but being able to trust its integrity.

🔵 The benefit:
Resilient navigation builds trust in every position.

Those who continuously monitor the integrity of their navigation data can detect disruptions early, minimize risks, and make informed decisions even under difficult conditions.

🟠 The ANavS solution:
With A-Shield, ANavS combines intelligent sensor fusion, multi-layer GNSS monitoring, AI-powered threat detection, and Galileo OSNMA. This enables early detection of tampering attempts and continuous assessment of the integrity of navigation data.

Because resilient navigation means more than just precision—it builds trust.

Der Beitrag What happens when you can no longer trust your position? erschien zuerst auf ANavS®.

👉 The biggest challenge for vehicle localization isn’t at the tunnel entrance, but at the tunnel exit.

👉 This is because the first GNSS signals are often still unstable there. Multipath effects, signal shadowing, and signal reflections can interfere with positioning.

🔵 How does ANavS solve this problem?
Through intelligent sensor fusion. While traveling through the tunnel, the vehicle’s position is continuously tracked using inertial sensors and other vehicle sensors. At the exit, returning GNSS signals are integrated into the localization only after a quality and plausibility check.

The result:
🟠 Continuous vehicle localization—before, during, and after the tunnel.
🟠 Fewer position jumps upon GNSS re-acquisition.
🟠 Resilient positioning even in challenging environments.

💡 It is precisely these seamless transitions that are crucial for resilient navigation.

Der Beitrag The biggest problem isn’t the GNSS outage! erschien zuerst auf ANavS®.

👉  A high-quality IMU delivers stable and precise motion data even when GNSS is temporarily unavailable.

🔵 This is exactly where the quality features that make all the difference come into play:

🟠 High bias stability

🟠 Low sensor noise

🟠 Precise calibration

🟠 Reliable performance across the entire temperature range

💡  In sensor fusion, a simple principle applies:

🟠  The quality of the results can never be better than the quality of the input data.

👉  A high-performance IMU therefore forms the foundation for precise position and motion information—and thus for robust navigation, even under demanding conditions.

Der Beitrag The best IMU isn’t the fastest ONE! erschien zuerst auf ANavS®.

🟠  This is precisely where the strength of sensor fusion comes into play. Through the intelligent combination of GNSS, IMU, camera, and LiDAR, the AI-ROX can enable reliable and highly precise positioning even when the GNSS signal is limited or completely absent.

🟠  Such real-world test environments are crucial for developing and validating navigation solutions for future automated and autonomous rail systems under conditions that closely resemble real-world operations.

🔗 Video about the project on the historic Genoa-Casella railway line

🔗 Project Description

Der Beitrag Precise positioning begins where GNSS reaches its limits erschien zuerst auf ANavS®.

🔵 It is not derived from a single sensor, but rather through intelligent fusion:
🟠 GNSS
🟠 IMU
🟠 LiDAR
🟠 Radar
🟠 Camera

👉 Only the precise synchronization of all sensor data delivers the accuracy required for development, testing, and validation.

💡 In the RepliCar research project, ANavS worked together with its project partners on high-precision reference sensor technology and data fusion to enable precisely this Ground Truth for sensor validation in automated driving. The insights gained form an important foundation for future developments in the field of autonomous mobility.

🔗 You can find more information about the research project RepliCar here and here.

Der Beitrag Precision starts with Ground Truth! erschien zuerst auf ANavS®.

🔵 How can this risk be reduced?
🟠 Sensor fusion detects inconsistencies between different data sources.
🟠 Galileo OSNMA helps verify authentic Galileo signals.
🟠 Redundant sensors increase the resilience and availability of the positioning solution.

💡 At ANavS, we build resilient navigation solutions for positioning you can trust.

Der Beitrag Can you trust your GNSS signal? erschien zuerst auf ANavS®.

👉 This was precisely the challenge ProRail, the Dutch rail infrastructure manager, set out to solve. A measurement system originally put out to bid did not meet the requirements—the project was discontinued after several years of development.

💡 Together with Nederlandse Spoorwegen (NS), the country’s largest passenger rail operator, ProRail developed RaM 2.0—a scalable measurement platform deployed directly on regular passenger trains. Instead of relying on dedicated measurement trains, sensor data is collected continuously during normal passenger operations.

Equipped with antennas and GNSS receivers, accelerometers and gyroscopes, the trains continuously monitor the condition of the railway infrastructure while remaining in daily service.

🔵 The added value is clear:
🟠 Early detection of infrastructure changes
🟠 Fewer unplanned disruptions
🟠 Higher network availability
🟠 The foundation for efficient predictive maintenance

🎉 We are proud that ANavS contributes to this forward-looking collaboration between ProRail and NS by supplying the GNSS receivers and antennas. Precise positioning is essential for transforming sensor data into reliable maintenance insights.

🎯 More data. Earlier insights. Higher availability.

Der Beitrag From reactive to predictive maintenance in rail infrastructure erschien zuerst auf ANavS®.