We present a dataset for evaluating the tracking accuracy of monocular visual odometry and SLAM methods. It contains 50 real-world sequences comprising more than 100 minutes of video, recorded across dozens of different environments -- ranging from narrow indoor corridors to wide outdoor scenes. All sequences contain mostly exploring camera motion, starting and ending at the same position. This allows to evaluate tracking accuracy via the accumulated drift from start to end, without requiring ground truth for the full sequence. In contrast to existing datasets, all sequences are photometrically calibrated. We provide exposure times for each frame as reported by the sensor, the camera response function, and dense lens attenuation factors. We also propose a novel, simple approach to non-parametric vignette calibration, which requires minimal set-up and is easy to reproduce. Finally, we thoroughly evaluate two existing methods (ORB-SLAM and DSO) on the dataset, including an analysis of the effect of image resolution, camera field of view, and the camera motion direction.
Many approaches exist to calibrate and remove vignetting artefacts and account for non-linear response functions. Early work focuses on image stitching and mosaicking, where the required calibration parameters need to be estimated from a small set of overlapping images @cite_14 @cite_11 @cite_13 . Since the available data is limited, such methods attempt to find low-dimensional (parametric) function representations, like radially symmetric polynomial representations for vignetting. More recent work @cite_17 @cite_15 has shown that such representations may not be sufficiently expressive to capture the complex nature of real-world lenses and hence advocate non-parametric -- dense -- vignetting calibration. In contrast to @cite_17 @cite_15 , our formulation however does not require a uniformly lit white paper , simplifying the required calibration set-up.
@cite_14 @cite_11 @cite_15 @cite_13 @cite_17
2159133693 2139851675 2018296774 2133132458
We discuss calibration and removal of "vignetting" (radial falloff) and exposure (gain) variations from sequences of images. Unique solutions for vignetting, exposure and scene radiances are possible when the response curve is known. When the response curve is unknown, an exponential ambiguity prevents us from recovering these parameters uniquely. However, the vignetting and exposure variations can nonetheless be removed from the images without resolving this ambiguity. Applications include panoramic image mosaics, photometry for material reconstruction, image-based rendering, and preprocessing for correlation-based vision algorithms. We discuss calibration and removal of “vignetting” (radial falloff) and exposure (gain) variations from sequences of images. Even when the response curve is known, spatially varying ambiguities prevent us from recovering the vignetting, exposure, and scene radiances uniquely. However, the vignetting and exposure variations can nonetheless be removed from the images without resolving these ambiguities or the previously known scale and gamma ambiguities. Applications include panoramic image mosaics, photometry for material reconstruction, image-based rendering, and preprocessing for correlation-based vision algorithms. This paper considers the problem of self-calibration of a camera from an image sequence in the case where the camera's internal parameters (most notably focal length) may change. The problem of camera self-calibration from a sequence of images has proven to be a difficult one in practice, due to the need ultimately to resort to non-linear methods, which have often proven to be unreliable. In a stratified approach to self-calibration, a projective reconstruction is obtained first and this is successively refined first to an affine and then to a Euclidean (or metric) reconstruction. It has been observed that the difficult step is to obtain the affine reconstruction, or equivalently to locate the plane at infinity in the projective coordinate frame. The problem is inherently non-linear and requires iterative methods that risk not finding the optimal solution. The present paper overcomes this difficulty by imposing chirality constraints to limit the search for the plane at infinity to a 3-dimensional cubic region of parameter space. It is then possible to carry out a dense search over this cube in reasonable time. For each hypothesised placement of the plane at infinity, the calibration problem is reduced to one of calibration of a nontranslating camera, for which fast non-iterative algorithms exist. A cost function based on the result of the trial calibration is used to determine the best placement of the plane at infinity. Because of the simplicity of each trial, speeds of over 10,000 trials per second are achieved on a 256 MHz processor. It is shown that this dense search allows one to avoid areas of local minima effectively and find global minima of the cost function. Nonuniform exposures often affect imaging systems, e.g., owing to vignetting. Moreover, the sensor’s radiometric response may be nonlinear. These characteristics hinder photometric measurements. They are particularly annoying in image mosaicking, in which images are stitched to enhance the field of view. Mosaics suffer from seams stemming from radiometric inconsistencies between raw images. Prior methods feathered the seams but did not address their root cause. We handle these problems in a unified framework. We suggest a method for simultaneously estimating the radiometric response and the camera nonuniformity, based on a frame sequence acquired during camera motion. The estimated functions are then compensated for. This permits image mosaicking, in which no seams are apparent. There is no need to resort to dedicated seam-feathering methods. Fundamental ambiguities associated with this estimation problem are stated.