Title:
Incremental Light Bundle Adjustment for Structure From Motion and Robotics
Incremental Light Bundle Adjustment for Structure From Motion and Robotics
Author(s)
Indelman, Vadim
Roberts, Richard
Dellaert, Frank
Roberts, Richard
Dellaert, Frank
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Abstract
Bundle adjustment (BA) is essential in many robotics and structure-from-motion applications. In robotics, often a bundle adjustment solution
is desired to be available incrementally as new poses and 3D points are
observed. Similarly in batch structure from motion, cameras are typically
added incrementally to allow good initializations. Current incremental
BA methods quickly become computationally expensive as more camera
poses and 3D points are added into the optimization. In this paper we
introduce incremental light bundle adjustment (iLBA), an efficient optimization framework that substantially reduces computational complexity compared to incremental bundle adjustment. First, the number of variables
in the optimization is reduced by algebraic elimination of observed
3D points, leading to a structureless BA. The resulting cost function is formulated
in terms of three-view constraints instead of re-projection errors
and only the camera poses are optimized. Second, the optimization problem
is represented using graphical models and incremental inference is applied,
updating the solution using adaptive partial calculations each time a
new camera is incorporated into the optimization. Typically, only a small
fraction of the camera poses are recalculated in each optimization step.
The 3D points, although not explicitly optimized, can be reconstructed
based on the optimized camera poses at any time. We study probabilistic
and computational aspects of iLBA and compare its accuracy against incremental
BA and another recent structureless method using real-imagery
and synthetic datasets. Results indicate iLBA is 2-10 times faster than
incremental BA, depending on number of image observations per frame.
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Date Issued
2015
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