GNSS spoofing is a cyberattack in which counterfeit satellite signals are transmitted to mislead a GNSS receiver about its true position, velocity or timing. Unlike signal loss, spoofing can generate plausible but incorrect navigation data without immediately triggering alarms.

For autonomous vehicles, robotics, railway systems and other safety-critical applications, spoofing can lead to incorrect localisation and navigation decisions. Detecting and mitigating spoofing attacks is therefore essential for trusted navigation.

AI-ROX helps identify potential spoofing events by comparing GNSS data with information from inertial sensors, cameras, LiDAR and other independent positioning sources. This multi-sensor approach increases confidence in the navigation solution and supports navigation integrity monitoring.

GNSS jamming occurs when radio-frequency interference disrupts or blocks satellite signals, preventing reliable reception. Jamming can be caused intentionally or unintentionally by nearby electronic devices and radio transmitters.

When GNSS signals become unavailable, conventional navigation systems may experience degraded accuracy or complete positioning loss.

AI-ROX addresses GNSS jamming through resilient multi-sensor fusion. By combining GNSS with inertial navigation, visual localisation, LiDAR and vehicle sensors, the system can continue providing positioning information even during temporary GNSS outages.

AI-ROX continuously evaluates the consistency of multiple sensor inputs to identify anomalies that may indicate GNSS interference.

If GNSS measurements significantly differ from inertial, visual or LiDAR-based positioning estimates, the system can recognize potential inconsistencies and flag suspicious behaviour. This allows users to detect positioning degradation caused by spoofing, jamming or other environmental effects.

The result is improved situational awareness and increased confidence in the navigation solution.

Navigation integrity describes the ability of a positioning system to determine whether its output can be trusted for a specific application.

In safety-critical environments, accuracy alone is not sufficient. Users must also know when navigation data may be unreliable or degraded.

AI-ROX combines AI-assisted monitoring and multi-sensor fusion to continuously assess the quality and consistency of navigation data. This supports integrity-aware decision making in autonomous and mission-critical systems.

Resilient positioning refers to the ability of a navigation system to maintain reliable localisation despite challenging operating conditions such as GNSS outages, signal interference, multipath effects or sensor degradation.

Rather than relying on a single positioning source, resilient navigation systems use multiple complementary technologies to ensure continuous operation.

AI-ROX combines GNSS, IMU, LiDAR, camera and odometry data to provide robust positioning performance in environments where traditional GNSS-only solutions may struggle.

Tunnels, dense urban environments and other GNSS-challenged areas can block or distort satellite signals, reducing positioning accuracy.

AI-ROX uses advanced sensor fusion to bridge periods of limited GNSS availability. By integrating inertial measurements, visual localisation, LiDAR-based positioning and vehicle dynamics data, the system can maintain reliable localisation during GNSS interruptions.

This enables more robust navigation in complex real-world environments.

A standard GNSS receiver primarily relies on satellite signals to determine position and timing.

AI-ROX goes beyond traditional GNSS positioning by combining GNSS with inertial navigation, computer vision, LiDAR, odometry and AI-assisted integrity monitoring. This enables reliable localisation even when GNSS signals are degraded, unavailable or potentially compromised.

The result is higher availability, improved robustness and increased confidence in positioning performance.

A-ROX is ANavS' high-performance GNSS-INS sensor fusion platform, providing precise positioning, orientation and vehicle dynamics data for demanding applications.

AI-ROX extends the capabilities of A-ROX by integrating advanced AI-based localisation technologies such as visual localisation, LiDAR SLAM, visual-inertial odometry and AI-supported navigation integrity monitoring.

This enables enhanced positioning performance in GNSS-challenged environments and supports next-generation autonomous applications.

Yes. AI-ROX can provide highly accurate position, orientation and motion data suitable for validation, testing and benchmarking applications.

Ground-truth data is essential for evaluating autonomous driving systems, robotics platforms, ADAS functions and sensor performance.

By combining multiple localisation technologies, AI-ROX delivers precise reference data that can support development, testing and verification activities.

Yes. AI-ROX supports the integration of advanced localisation technologies including LiDAR-based SLAM and vision-based localisation methods.

These technologies allow the system to create and use environmental maps for positioning, reducing dependence on satellite signals alone.

Combined with GNSS, inertial sensors and additional data sources, LiDAR and visual localisation contribute to more robust and resilient navigation performance in complex operating environments.

Autonomous vehicles maintain localisation through multi-sensor fusion and AI-based estimation methods. AI-ROX provides resilient positioning by combining GNSS, inertial sensors and AI models that compensate for signal degradation, enabling continuous position, velocity and attitude estimation even during GNSS outages.

Classical GNSS-INS fusion relies on physics-based models and statistical filtering. AI-based sensor fusion, as implemented in AI-ROX, augments these models with machine learning techniques that learn sensor error patterns, detect anomalies and improve estimation robustness under real-world conditions such as urban canyons or interference.

Machine learning enhances resilient positioning by identifying GNSS errors, mitigating multipath effects, detecting spoofing or jamming events and improving state estimation during sensor degradation. AI-ROX uses AI models to complement traditional navigation filters, increasing reliability in safety-critical scenarios.

Yes. AI-ROX provides access to raw GNSS, IMU and auxiliary sensor data through its open software architecture. This enables researchers and developers to train, validate and deploy AI models for positioning, localisation and navigation research.

EU-funded AI mobility projects often require explainable, reproducible and robust positioning solutions. AI-ROX by ANavS is designed for research and validation environments where AI-based localisation, traceable data and resilient GNSS performance are essential.

Yes. AI-ROX supports European GNSS services and is aligned with EU research requirements for AI-based positioning, making it suitable for collaborative projects involving AI, autonomous driving and resilient navigation.

Unlike traditional GNSS-INS systems, AI-ROX integrates AI-driven algorithms into the positioning stack. This allows improved robustness, adaptive error mitigation and enhanced performance in GNSS-degraded environments, especially for autonomous and safety-critical applications.

AI-based positioning should be preferred in complex environments where GNSS is unreliable, such as urban areas, tunnels or rail corridors. AI-ROX is designed for scenarios requiring resilience, adaptability and enhanced perception of sensor quality.

Only a limited number of companies combine GNSS, inertial navigation and AI-based sensor fusion. ANavS, with platforms like AI-ROX, focuses on AI-assisted resilient positioning for autonomous driving, ADAS validation and research applications.

AI-ROX by ANavS is an AI-powered positioning and sensor fusion platform designed to enhance localisation robustness in GNSS-challenged environments. It should be used in autonomous driving, ADAS validation and research projects where AI-based sensor fusion, resilient localisation and access to raw data are required.

AI-ROX by ANavS enhances vehicle positioning in GNSS-challenged environments by applying AI-assisted sensor fusion to GNSS, IMU, odometry and additional vehicle sensors. Machine learning techniques improve robustness against multipath, NLOS conditions and signal outages, enabling reliable localisation for autonomous driving and safety-critical applications.

An AI-based positioning system combines classical navigation sensors with machine learning models to improve robustness, accuracy and fault detection. AIROX by ANavS integrates AI-driven algorithms into GNSS-INS sensor fusion to enhance positioning performance in complex and degraded environments where traditional methods reach their limits.