Updating and Revising Star Camera for Future Flights of Balloon Borne Experiment

The BLAST (Balloon-borne Large Aperture Submillimeter Telescope) experiment surveys the galaxy from altitudes of 100,000 ft in order to answer important cosmological questions, such as how stars are formed. This experiment is conducted above Antarctica to minimize unwanted noise. Two star cameras are used in the navigation systems to identify known stars. The cameras take pictures and match stars in the image to known star positions from a catalog stored in the star camera\u27s computer. This is done using code written in C++, a computer programming language. In order to modernize the system, the code needs to be updated. A camera that has flown multiple missions was switched from a legacy codebase that was used in past missions, to the star tracking and attitude reconstruction (STARS) code, designed for the E and B Experiment (EBEX), a similar, balloon-borne experiment. This switch required cataloging the parts of the camera, testing the camera with the legacy code, and adapting the new code for this particular camera. The result is that the camera takes pictures and identifies stars using the new code. The next step is to suggest physical changes to the camera\u27s hardware to improve performance using the new code

Bound and Conquer: Improving Triangulation by Enforcing Consistency

We study the accuracy of triangulation in multi-camera systems with respect to the number of cameras. We show that, under certain conditions, the optimal achievable reconstruction error decays quadratically as more cameras are added to the system. Furthermore, we analyse the error decay-rate of major state-of-the-art algorithms with respect to the number of cameras. To this end, we introduce the notion of consistency for triangulation, and show that consistent reconstruction algorithms achieve the optimal quadratic decay, which is asymptotically faster than some other methods. Finally, we present simulations results supporting our findings. Our simulations have been implemented in MATLAB and the resulting code is available in the supplementary material.Comment: 8 pages, 4 figures, Submitted to IEEE Transactions on Pattern Analysis and Machine Intelligenc

Performance prediction of point-based three-dimensional volumetric measurement systems

Point-based three-dimensional volumetric measurement systems are defined as multi-view vision systems which reconstruct a three-dimensional scene by first identifying key points on the views and then performing the reconstruction. Examples of these are defocusing digital particle image velocimetry (DDPIV) (Pereira et al 2000 Exp. Fluids 29 S78–84) and 3D particle tracking velocimetry (3DPTV) (Papantoniou and Maas 1990 5th Int. Symp. on the Application of Laser Techniques in Fluid Mechanics) which reconstruct clouds of flow tracers in order to estimate flow velocities. The reconstruction algorithms in these systems are variations of an epipolar line search. This paper presents a generalized error analysis of such methods, both in reconstruction precision (error in the reconstructed scene) and reconstruction quality (number of ambiguities or 'ghosts' produced)