The rolling shutter camera has received great attention due to its low cost imaging capability, however, the estimation of relative pose between rolling shutter cameras still remains a difficult problem owing to its line-by-line image capturing characteristics. To alleviate this problem, we exploit gyroscope measurements, angular velocity, along with image measurement to compute the relative pose between rolling shutter cameras. The gyroscope measurements provide the information about instantaneous motion that causes the rolling shutter distortion. Having gyroscope measurements in one hand, we simplify the relative pose estimation problem and find a minimal solution for the problem based on the Grobner basis polynomial solver. The proposed method requires only five points to compute relative pose between rolling shutter cameras, whereas previous methods require 20 or 44 corresponding points for linear and uniform rolling shutter geometry models, respectively. Experimental results on synthetic and real data verify the superiority of the proposed method over existing relative pose estimation methods.
@cite_30: In this paper we consider critical motion sequences (CMSs) of rolling-shutter (RS) SfM. Employing an RS camera model with linearized pure rotation, we show that the RS distortion can be approximately expressed by two internal parameters of an "imaginary" camera plus one-parameter nonlinear transformation similar to lens distortion. We then reformulate the problem as self-calibration of the imaginary camera, in which its skew and aspect ratio are unknown and varying in the image sequence. In the formulation, we derive a general representation of CMSs. We also show that our method can explain the CMS that was recently reported in the literature, and then present a new remedy to deal with the degeneracy. Our theoretical results agree well with experimental results; it explains degeneracies observed when we employ naive bundle adjustment, and how they are resolved by our method.
@cite_35: Camera phones are a promising platform for hand-held augmented reality. As their computational resources grow, they are becoming increasingly suitable for visual tracking tasks. At the same time, they still offer considerable challenges: Their cameras offer a narrow field-of-view not best suitable for robust tracking; images are often received at less than 15Hz; long exposure times result in significant motion blur; and finally, a rolling shutter causes severe smearing effects. This paper describes an attempt to implement a keyframe-based SLAMsystem on a camera phone (specifically, the Apple iPhone 3G). We describe a series of adaptations to the Parallel Tracking and Mapping system to mitigate the impact of the device's imaging deficiencies. Early results demonstrate a system capable of generating and augmenting small maps, albeit with reduced accuracy and robustness compared to SLAM on a PC.
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@cite_0: Artefacts that are present in images taken from a moving rolling shutter camera degrade the accuracy of absolute pose estimation. To alleviate this problem, we introduce an addition linear velocity in the camera projection matrix to approximate the motion of the rolling shutter camera. In particular, we derive a minimal solution using the Grobner Basis that solves for the absolute pose as well as the motion of a rolling shutter camera. We show that the minimal problem requires 5-point correspondences and gives up to 8 real solutions. We also show that our formulation can be extended to use more than 5-point correspondences. We use RANSAC to robustly get all the inliers. In the final step, we relax the linear velocity assumption and do a non-linear refinement on the fuli motion, i.e. linear and angular velocities, and pose of the rolling shutter camera with all the inliers. We verify the feasibility and accuracy of our algorithm with both simulated and real-world datasets.
@cite_2: This paper introduces a bundle adjustment (BA) method that obtains accurate structure and motion from rolling shutter (RS) video sequences: RSBA. When a classical BA algorithm processes a rolling shutter video, the resultant camera trajectory is brittle, and complete failures are not uncommon. We exploit the temporal continuity of the camera motion to define residuals of image point trajectories with respect to the camera trajectory. We compare the camera trajectories from RSBA to those from classical BA, and from classical BA on rectified videos. The comparisons are done on real video sequences from an iPhone 4, with ground truth obtained from a global shutter camera, rigidly mounted to the iPhone 4. Compared to classical BA, the rolling shutter model requires just six extra parameters. It also degrades the sparsity of the system Jacobian slightly, but as we demonstrate, the increase in computation time is moderate. Decisive advantages are that RSBA succeeds in cases where competing methods diverge, and consistently produces more accurate results.
@cite_10: We address the problem of Structure from Motion (SfM) with rolling shutter cameras. We first show that many common camera configurations, e.g. cameras with parallel readout directions, become critical and allow for a large class of ambiguities in multi-view reconstruction. We provide mathematical analysis for one, two and some multi-view cases and verify it by synthetic experiments. Next, we demonstrate that bundle adjustment with rolling shutter cameras, which are close to critical configurations, may still produce drastically deformed reconstructions. Finally, we provide practical recipes how to photograph with rolling shutter cameras to avoid scene deformations in SfM. We evaluate the recipes and provide a quantitative analysis of their performance in real experiments. Our results show how to reconstruct correct 3D models with rolling shutter cameras.
@cite_12: The majority of consumer quality cameras sold today have CMOS sensors with rolling shutters. In a rolling-shutter camera, images are read out row by row, and thus each row is exposed during a different time interval. A rolling-shutter exposure causes geometric image distortions when either the camera or the scene is moving, and this causes state-of-the-art structure and motion algorithms to fail. We demonstrate a novel method for solving the structure and motion problem for rolling-shutter video. The method relies on exploiting the continuity of the camera motion, both between frames, and across a frame. We demonstrate the effectiveness of our method by controlled experiments on real video sequences. We show, both visually and quantitatively, that our method outperforms standard structure and motion, and is more accurate and efficient than a two-step approach, doing image rectification and structure and motion.
@cite_11: A huge fraction of cameras used nowadays is based on CMOS sensors with a rolling shutter that exposes the image line by line. For dynamic scenes cameras this introduces undesired effects like stretch, shear and wobble. It has been shown earlier that rotational shake induced rolling shutter effects in hand-held cell phone capture can be compensated based on an estimate of the camera rotation. In contrast, we analyse the case of significant camera motion, e.g. where a bypassing street level capture vehicle uses a rolling shutter camera in a 3D reconstruction framework. The introduced error is depth dependent and cannot be compensated based on camera motion rotation alone, invalidating also rectification for stereo camera systems. On top, significant lens distortion as often present in wide angle cameras intertwines with rolling shutter effects as it changes the time at which a certain 3D point is seen. We show that naive 3D reconstructions (assuming global shutter) will deliver biased geometry already for very mild assumptions on vehicle speed and resolution. We then develop rolling shutter dense multiview stereo algorithms that solve for time of exposure and depth at the same time, even in the presence of lens distortion and perform an evaluation on ground truth laser scan models as well as on real street-level data.
Besides, SfM and SLAM based on a monocular rolling shutter camera have been intensively studied taking into account the rolling shutter effects. Klein and Murray @cite_35 used the constant velocity model in the SLAM framework to predict and correct the rolling shutter distortion occurring in the next frame. Hedborg al @cite_12 @cite_2 applied the rolling shutter camera projection model to the non-linear optimization step of SfM, bundle adjustment. Their key idea is to exploit temporal continuity of the camera motion on the video input to deal with the rolling shutter distortion. Saurer al @cite_11 also considered the rolling shutter distortion in dense 3D reconstruction and they also proposed a minimal solution for absolute pose estimation problem of rolling shutter cameras @cite_0 . Albl al @cite_10 analyzed the degeneracy of the rolling shutter SfM and suggested how to avoid the degeneracy when shooting videos. Recently, Ito and Okatani @cite_30 derived the degeneracy of rolling shutter SfM as the general expression through a self-calibration-based approach. Zhuang al @cite_3 proposed a constant acceleration model for relative pose estimation and image rectification in two consecutive images.