It is well known that bundle adjustment is the optimal non-linear least-squares formulation of the simultaneous localization and mapping problem, in that its maximum likelihood form matches the definition of the Cramer Rao Lower Bound. Unfortunately, computing the ML solution is often prohibitively expensive – this is especially true during loop closures, which often necessitate adjusting all p...

We investigate the suitability of different local feature detectors for the task of automatic image orientation under different scene texturings. Building on an existing system for image orientation, we vary the applied operators while keeping the strategy fixed, and evaluate the results. An emphasis is put on the effect of combining detectors for calibrating difficult datasets. Besides some of...

There is currently an abundance of vision algorithms which, provided with a sequence of images that have been acquired from sufficiently close successive 3D locations, are capable of determining the relative positions of the viewpoints from which the images have been captured. However, very few of these algorithms can cope with unordered image sets. This paper presents an efficient method for r...

In classical photogrammetry, point observations are manually determined by an operator for performing the bundle adjustment of a sequence of images. In such cases, a comparison of different estimates is usually carried out with respect to the estimated 3D object points. Today, a broad range of automatic methods are available for extracting and matching point features across images, even in the ...

Bundle adjustment (BA) is an optimization process refining the estimates of extrinsic camera parameters (position and orientation, or pose) and the three dimensional (3D) positions of features using overlapping images from multiple views. The optimization in BA may be formulated many ways. We compare four alternatives, including the standard formulation, from the perspectives of the region of c...

The recent resurgence of stereoscopic 3D films has triggered a high demand for post-processing tools for stereoscopic image sequences. Camera motion estimation, also known as structure-from-motion (SfM) or match-moving, is an essential step in the post-processing pipeline. In order to ensure a high accuracy of the estimated camera parameters, a bundle adjustment algorithm should be employed. We...

We present the design and implementation of a new inexact Newton type algorithm for solving large-scale bundle adjustment problems with tens of thousands of images. We explore the use of Conjugate Gradients for calculating the Newton step and its performance as a function of some simple and computationally efficient preconditioners. We show that the common Schur complement trick is not limited ...

Significant instrumental systematic errors are known to exist in data captured with range cameras using lock-in pixel technology. Because they are independent of the imaged object scene structure, these errors can be rigorously estimated in a self-calibrating bundle adjustment procedure. This paper presents a review and a quantitative comparison of three methods for range camera self-calibratio...