Structure and Velocity Estimation of a Moving Object via Synthetic Persistence by a Network of Stationary Cameras
Zachary I. Bell, Christian G. Harris, Runhan Sun, Warren E. Dixon · 2019
Data-based, exponentially converging observers are developed for a network of stationary cooperative cameras estimating the Euclidean distance to features on a moving object (and hence, the objects' accurately scaled structure), without requiring the typical positive depth constraint and only requiring the object to remain in one camera's field-of-view. The developed observers demonstrate that a synthetic persistent view of the object relative to each camera is sufficient to maintain distance estimates. A Lyapunov-based stability analysis demonstrates that the developed geometric approach enables the developed distance observers for each camera to exponentially converge using the synthetic image of the object features generated by neighboring cameras.