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.