• The first challenge is posed by the large number of features (typically ≥50) that a typical computerized vision system can recognize during any given frame period. The large number of features imposes a heavy burden for real-time computation.
• The second challenge is associated with the lack of range information when camera measurements are the only measurements available. Camera’s measurements consist only of bearings to specific feature points in images. The PFT data type is especially challenging inasmuch as recognized features do not necessarily represent known objects and do not contain location information.
• The third challenge is posed by the fact that computer vision information often relates to images taken in the past. For example, the PFT data type reports features that were recognized as being common to two images taken at earlier times. The need to update the current state estimate by use of information from the past presents a challenge because prior recursive state-estimating algorithms typically only propagate the current state.

The present algorithm addresses these challenges by incorporating the following innovations:

The first innovation is a preprocessing step, based on QR factorization (a particular matrix factorization, a description of which would exceed the scope of this article), that provides for optimal compression of LMT, PFT, and RPT updates that involve large numbers of recognized features. This compression eliminates the need for a considerable amount of real-time computation.

The second innovation is a mathematical annihilation method for forming a linear measurement equation from the PFT data. The annihilation method is equivalent to a mathematical projection that eliminates the dependence on the unknown scale factor.

The third innovation is a state-augmentation method for handling PFT and other data types that relate states from two or more past instants of time. The state-augmentation method stands in contrast to a prior stochastic cloning method. State augmentation provides an optimal solution to the state-estimation problem, while stochastic cloning can be shown to be suboptimal.

This work was done by David Bayard and Paul Brugarolas of Caltech for NASA’s Jet Propulsion Laboratory.

The software used in this innovation is available for commercial licensing. Please contact Karina Edmonds of the California Institute of Technology at (626) 395-2322. Refer to NPO-41321.