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Parallax Bundle Adjustment on Manifold with Convexified Initialization by Liu, Liyang; Zhang, Teng; Liu, Yi; Leighton, Brenton; Zhao, Liang; Huang, Shoudong; Dissanayake, Gamini is a scholarly article available to read on EtoBox.

What is Parallax Bundle Adjustment on Manifold with Convexified Initialization about?

Bundle adjustment (BA) with parallax angle based feature parameterization has been shown to have superior performance over BA using inverse depth or XYZ feature forms. In this paper, we propose an improved version of the parallax BA algorithm (PMBA) by extending it to the manifold domain along with observation-ray based objective function. With this modification, the problem formulation faithfully mimics the projective nature in a camera's image formation, BA is able to achieve better convergence, accuracy and robustness. This is particularly useful in handling diverse outdoor environments and collinear motion modes. Capitalizing on these properties, we further propose a pose-graph simplification to PMBA, with significant dimensionality reduction. This pose-graph model is convex in nature, easy to solve and its solution can serve as a good initial guess to the original BA problem which is intrinsically non-convex. We provide theoretical proof that our global initialization strategy can guarantee a near-optimal solution. Using a series of experiments involving diverse environmental conditions and motions, we demonstrate PMBA's superior convergence performance in comparison to other

Author

Liu, Liyang; Zhang, Teng; Liu, Yi; Leighton, Brenton; Zhao, Liang; Huang, Shoudong; Dissanayake, Gamini

Published

2018

Language

EN