An algorithm and software to implement the algorithm are being developed as means to estimate the state (that is, the position and velocity) of an autonomous vehicle, relative to a visible nearby target object, to provide guidance for maneuvering the vehicle. In the original intended application, the autonomous vehicle would be a spacecraft and the nearby object would be a small astronomical body (typically, a comet or asteroid) to be explored by the spacecraft. The algorithm could also be used on Earth in analogous applications — for example, for guiding underwater robots near such objects of interest as sunken ships, mineral deposits, or submerged mines.
For the purpose of the algorithm, it is assumed that the robot would be equipped with a vision system that would include one or more electronic cameras, image-digitizing circuitry, and an imagedata- processing computer that would generate feature-recognition data products. Such products customarily include bearing angles of lines of sight from the camera(s) [and, hence, from the vehicle] to recognized features. The data products that are processed by the present algorithm are of the following types:
- The Cartesian vector from the camera to a reference point on or in the target body;
- Bearing angles from the camera to the reference point;
- A landmark table (LMT);
- A paired-feature table (PFT); and
- A range point table (RPT).
The incorporation of the LMT and PFT is particularly important. LMT and PFT data are generated by typical computer- vision systems that could be used in the contemplated applications. In an LMT, a vision system recognizes landmarks from an onboard catalog and reports their bearing angles and associated known locations on the target body. In a PFT, a vision system reports bearing angles to features recognized as being common to two images taken at different times. Relative to the LMT, the PFT can be generated with less computation because it is necessary only to track features frame-to-frame; it is not necessary to associate the features with landmarks. However, it is more challenging to incorporate the PFT in a state-estimation algorithm for reasons discussed below. The LMT and PFT are complementary in the sense that the LMT provides positiontype information while the PFT provides velocity-type information. However, the velocity-type information from the PFT is incomplete because it includes an unknown scale factor. A state-estimation algorithm must fuse the aforementioned data types to make an optimal estimate.