Vision AI develops AI-powered solutions for precise geolocalization and semantic 3D mapping. These advanced spatial awareness tools are specifically engineered to support the operational needs of autonomous systems. The platform provides detailed environmental context necessary for reliable navigation and decision-making in complex, real-world scenarios.
Funding
Funding not disclosed
Founders
Product
Problem
Autonomous platforms—such as self-driving cars, delivery drones, and mobile robots—require centimeter‑level geolocation and a detailed semantic understanding of their surroundings to navigate safely. Existing mapping pipelines often rely on sparse point clouds or manually curated maps, leading to latency, limited scalability, and reduced reliability in dynamic environments.
Solution
The company delivers an AI‑driven geolocalization engine that fuses LiDAR, camera, and GNSS data to generate high‑resolution, semantically annotated 3D maps in near real time. Deep neural networks perform feature extraction, object classification, and terrain labeling, producing a digital twin that reflects both geometry and contextual information such as road markings, vegetation, and infrastructure. The platform operates on edge hardware for low‑latency pose estimation while synchronizing updates to a cloud‑hosted map repository for fleet‑wide consistency. Clients can access the maps via RESTful APIs or ROS‑compatible interfaces, enabling seamless integration with existing perception and planning stacks. Continuous learning pipelines automatically incorporate new sensor data, reducing the need for manual map maintenance and improving accuracy over time.
Features
- Multi‑sensor fusion pipeline that combines LiDAR, stereo vision, and GNSS to achieve sub‑centimeter positioning accuracy
- Semantic segmentation models that label 3D point clouds with over 50 object classes, including dynamic agents and static infrastructure
- Real‑time SLAM algorithm optimized for edge GPUs, delivering pose updates at 30 Hz with < 10 ms latency
- Cloud‑native map database with version control, differential updates, and tile‑based streaming for large‑scale environments
- ROS 2 and OpenAPI endpoints for direct integration with autonomous stack components
- Automated map refinement loop that retrains models on newly collected data to improve long‑term map fidelity
- Built‑in security layer with TLS encryption, role‑based access control, and audit logging for compliance‑critical deployments