A system for highly precise navigation uses visual and inertial- based measurements that feeds into a unique Kalman filter based algorithm for pose estimation (position and orientation). The pose estimation algorithm can provide a unified basis for stability control traction control slip detection and obstacle avoidance in ground-based applications and navigation and tracking in air-based applications. The system operates where GPS and odometer systems fail or are denied and can be integrated into existing automatic active safety systems and aerospace navigation systems.
1) Combines vision and inertial sensing (similar to human perception) 2) Kalman filter-based algorithm generates pose estimation (position and orientation) information which enables faster and more robust tracking 3) High accuracy and low computational complexity in highly cluttered ‘real-world’ environments 4) Higher accuracy and lower cost than radar-based systems 5) Operates where GPS/odometry systems may fail 6) Can be integrated in existing automotive active safety systems or unmanned aerial vehicle navigation systems