Localization is a key component of autonomous systems. Artisense solves the challenge of localization using computer vision and machine learning techniques. This approach vastly outperforms traditional RTK GNSS and INS solutions in accuracy and integrity.
Artisense’ product is built on 15 years of deep-tech research by Prof. Dr. Daniel Cremers, Chair of Computer Vision at Technical University Munich, Germany. Our deep-tech DNA paired with engineering and productization capabilities uniquely positions Artisense to solve the key challenge of simultaneous localization and mapping (SLAM) for autonomous systems. Driven by the passion to move things forward, Artisense continues to produce new IP and license it to leading public and private industry partners.
Accurate localization in GNSS denied environments is a challenge. Artisense offers Visual-Inertial Navigation Systems (VINS) that enable localization in any environment.
State-of-the-art performance is achieved by proprietary SLAM algorithm which fuses RTK GNSS, camera, IMU, and other sensor modalities in a tightly-coupled optimization.
The system provides reliable location accuracy, necessary for autonomous driving and mobile robotics use cases.
Mapping & Spatial Analytics
Mapping large scale environments accurately and efficiently is key for autonomous navigation and spatial analytics. Artisense offers Mobile Mapping System (MMS) that helps our customers digitize and navigate the world.
Artisense’ automatic data collection and processing pipelines enable fast turn-around times for surveying and feature extraction. This enables automated map update workflows, necessary for scalable autonomous navigation.