  Most methods for Bundle Adjustment (BA) in computer vision are either
centralized or operate incrementally. This leads to poor scaling and affects
the quality of solution as the number of images grows in large scale structure
from motion (SfM). Furthermore, they cannot be used in scenarios where image
acquisition and processing must be distributed. We address this problem with a
new distributed BA algorithm. Our distributed formulation uses alternating
direction method of multipliers (ADMM), and, since each processor sees only a
small portion of the data, we show that robust formulations improve
performance. We analyze convergence of the proposed algorithm, and illustrate
numerical performance, accuracy of the parameter estimates, and scalability of
the distributed implementation in the context of synthetic 3D datasets with
known camera position and orientation ground truth. The results are comparable
to an alternate state-of-the-art centralized bundle adjustment algorithm on
synthetic and real 3D reconstruction problems. The runtime of our
implementation scales linearly with the number of observed points.
