![]() We also show that, without incremental initialization or via third-party information, our global initialization process helps to bootstrap the full BA successfully in various scenarios, sequential or out-of-order, including some datasets from the "Bundle Adjustment in the Large" database.Ī strong requirement to deploy autonomous mobile robots is their capacity to localize themselves with a certain precision in relation to their environment. Using a series of experiments involving diverse environmental conditions and motions, we demonstrate PMBA's superior convergence performance in comparison to other BA methods. We provide theoretical proof that our global initialization strategy can guarantee a near-optimal solution. 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. Capitalizing on these properties, we further propose a pose-graph simplification to PMBA, with significant dimensionality reduction. ![]() This is particularly useful in handling diverse outdoor environments and collinear motion modes. 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. 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. ![]() Especially, since the relative scales are optimized automatically in the proposed BA and map joining algorithms, there is no need to compute any relative scales even for a loop more than 1km.īundle adjustment (BA) with parallax angle based feature parameterization has been shown to have superior performance over BA using inverse depth or XYZ feature forms. Extensive simulations and a publicly available large-scale real dataset with centimeter accuracy ground truth are used to demonstrate the accuracy and consistency of the BA and map joining algorithms using the new parametrization. A new map joining algorithm that allows combining a sequence of local maps generated using BA with the proposed parametrization, that avoids the large computational cost of a global BA, and can automatically optimize the relative scales of the local maps without any loss of information, is also presented. A new bundle adjustment (BA) algorithm using the proposed parallax angle parametrization is developed and shown to be more reliable as compared with existing BA algorithms that use Euclidean XYZ or inverse depth parametrizations. The parametrization is based on the parallax angle and can reliably represent both nearby and distant features, as well as features in the direction of camera motion and features observed only once. This paper presents a new unified feature parametrization approach for monocular SLAM.
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