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14
A linear approach to motion estimation using generalized camera models
 In: CVPR
, 2008
"... A wellknown theoretical result for motion estimation using the generalized camera model is that 17 corresponding image rays can be used to solve linearly for the motion of a generalized camera. However, this paper shows that for many common configurations of the generalized camera models (e.g., mul ..."
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Cited by 22 (7 self)
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A wellknown theoretical result for motion estimation using the generalized camera model is that 17 corresponding image rays can be used to solve linearly for the motion of a generalized camera. However, this paper shows that for many common configurations of the generalized camera models (e.g., multicamera rig, catadioptric camera etc.), such a simple 17point algorithm does not exist, due to some previously overlooked ambiguities. We further discover that, despite the above ambiguities, we are still able to solve the motion estimation problem effectively by a new algorithm proposed in this paper. Our algorithm is essentially linear, easy to implement, and the computational efficiency is very high. Experiments on both real and simulated data show that the new algorithm achieves reasonably high accuracy as well. 1.
Absolute Scale in Structure from Motion from a . . .
, 2009
"... In structurefrommotion with a single camera it is well known that the scene can be only recovered up to a scale. In order to compute the absolute scale, one needs to know the baseline of the camera motion or the dimension of at least one element in the scene. In this paper, we show that there exis ..."
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Cited by 17 (2 self)
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In structurefrommotion with a single camera it is well known that the scene can be only recovered up to a scale. In order to compute the absolute scale, one needs to know the baseline of the camera motion or the dimension of at least one element in the scene. In this paper, we show that there exists a class of structurefrommotion problems where it is possible to compute the absolute scale completely automatically without using this knowledge, that is, when the camera is mounted on wheeled vehicles (e.g. cars, bikes, or mobile robots). The construction of these vehicles puts interesting constraints on the camera motion, which are known as “nonholonomic constraints”. The interesting case is when the camera has an offset to the vehicle’s center of motion. We show that by just knowing this offset, the absolute scale can be computed with a good accuracy when the vehicle turns. We give a mathematical derivation and provide experimental results on both simulated and real data over a large image dataset collected during a 3 Km path. To our knowledge this is the first time nonholonomic constraints of wheeled vehicles are used to estimate the absolute scale. We believe that the proposed method can be useful in those research areas involving visual odometry and mapping with vehicle mounted cameras.
Motion estimation for nonoverlapping multicamera rigs: Linear algebraic and l∞ geometric solutions
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2010
"... We investigate the problem of estimating the egomotion of a multicamera rig from two positions of the rig. We describe and compare two new algorithms for finding the 6 degrees of freedom (3 for rotation and 3 for translation) of the motion. One algorithm gives a linear solution and the other is a ..."
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Cited by 14 (4 self)
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We investigate the problem of estimating the egomotion of a multicamera rig from two positions of the rig. We describe and compare two new algorithms for finding the 6 degrees of freedom (3 for rotation and 3 for translation) of the motion. One algorithm gives a linear solution and the other is a geometric algorithm that minimizes the maximum measurement error—the optimal L1 solution. They are described in the context of the General Camera Model (GCM), and we pay particular attention to multicamera systems in which the cameras have nonoverlapping or minimally overlapping field of view. Many nonlinear algorithms have been developed to solve the multicamera motion estimation problem. However, no linear solution or guaranteed optimal geometric solution has previously been proposed. We made two contributions: 1) a fast linear algebraic method using the GCM and 2) a guaranteed globally optimal algorithm based on the L1 geometric error using the branchandbound technique. In deriving the linear method using the GCM, we give a detailed analysis of degeneracy of camera configurations. In finding the globally optimal solution, we apply a rotation space search technique recently proposed by Hartley and Kahl. Our experiments conducted on both synthetic and real data have shown excellent results.
S.B.: Plenoptic flow: Closedform visual odometry for light field cameras
 In: Proceedings, 2011 IEEE/RSJ international conference on intelligent robots and systems (IROS
, 2011
"... Abstract—Three closedform solutions are proposed for six degree of freedom (6DOF) visual odometry for light field cameras. The first approach breaks the problem into geometrically driven subproblems with solutions adaptable to specific applications, while the second generalizes methods from opti ..."
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Cited by 9 (7 self)
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Abstract—Three closedform solutions are proposed for six degree of freedom (6DOF) visual odometry for light field cameras. The first approach breaks the problem into geometrically driven subproblems with solutions adaptable to specific applications, while the second generalizes methods from optical flow to yield a more direct approach. The third solution integrates elements into a remarkably simple equation of plenoptic flow which is directly solved to estimate the camera’s motion. The proposed methods avoid feature extraction, operating instead on all measured pixels, and are therefore robust to noise. The solutions are closedform, computationally efficient, and operate in constant time regardless of scene complexity, making them suitable for realtime robotics applications. Results are shown for a simulated underwater survey scenario, and realworld results demonstrate good performance for a threecamera array, outperforming a stateoftheart stereo featuretracking approach. I.
Measuring camera translation by the dominant apical angle
 IEEE Conference on Computer Vision and Pattern Recognition, CVPR
, 2008
"... This paper provides a technique for measuring camera translation relatively w.r.t. the scene from two images. We demonstrate that the amount of the translation can be reliably measured for general as well as planar scenes by the most frequent apical angle, the angle under which the camera centers ar ..."
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Cited by 9 (7 self)
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This paper provides a technique for measuring camera translation relatively w.r.t. the scene from two images. We demonstrate that the amount of the translation can be reliably measured for general as well as planar scenes by the most frequent apical angle, the angle under which the camera centers are seen from the perspective of the reconstructed scene points. Simulated experiments show that the dominant apical angle is a linear function of the length of the true camera translation. In a real experiment, we demonstrate that by skipping image pairs with too small motion, we can reliably initialize structure from motion, compute accurate camera trajectory in order to rectify images and use the ground plane constraint in recognition of pedestrians in a handheld video sequence. 1.
A New Minimal Solution to the Relative Pose of a Calibrated Stereo Camera with Small Field of View Overlap
"... In this paper we present a new minimal solver for the relative pose of a calibrated stereo camera. It is based on the observation that a feature visible in all cameras constrains the relative pose of the second stereo camera to be on a sphere around the feature which has a known position relative to ..."
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Cited by 5 (3 self)
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In this paper we present a new minimal solver for the relative pose of a calibrated stereo camera. It is based on the observation that a feature visible in all cameras constrains the relative pose of the second stereo camera to be on a sphere around the feature which has a known position relative to the first stereo camera pose due to its triangulation. The constraint leaves three degrees of freedom, two for the location of the second camera on the sphere and the third for rotation in the plane tangent to the sphere. We use three temporal 2D correspondences, two correspondences from the left (or right) camera and one correspondence from the other camera to solve for these three remaining degrees of freedom. This approach is amenable to stereo pairs having a small overlap in their views. We present an efficient solution of this novel relative pose problem, theoretically derive how to use our new solver with two classes of measurements in RANSAC, evaluate its performance given noise and outliers and demonstrate its use in a realtime structure from motion system. 1.
Motion estimation for selfdriving cars with a generalized camera
 In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013. doi: 10.1109/CVPR. 2013.354. URL http://ieeexplore.ieee.org/xpls/abs all. jsp?arnumber=6619198
"... In this paper, we present a visual egomotion estimation algorithm for a selfdriving car equipped with a closetomarket multicamera system. By modeling the multicamera system as a generalized camera and applying the nonholonomic motion constraint of a car, we show that this leads to a novel 2 ..."
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Cited by 4 (3 self)
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In this paper, we present a visual egomotion estimation algorithm for a selfdriving car equipped with a closetomarket multicamera system. By modeling the multicamera system as a generalized camera and applying the nonholonomic motion constraint of a car, we show that this leads to a novel 2point minimal solution for the generalized essential matrix where the full relative motion including metric scale can be obtained. We provide the analytical solutions for the general case with at least one intercamera correspondence and a special case with only intracamera correspondences. We show that up to a maximum of 6 solutions exist for both cases. We identify the existence of degeneracy when the car undergoes straight motion in the special case with only intracamera correspondences where the scale becomes unobservable and provide a practical alternative solution. Our formulation can be efficiently implemented within RANSAC for robust estimation. We verify the validity of our assumptions on the motion model by comparing our results on a large realworld dataset collected by a car equipped with 4 cameras with minimal overlapping fieldofviews against the GPS/INS ground truth. 1.
A Visual Servoing Model for Generalised Cameras: Case study of nonoverlapping cameras
 in "IEEE Int. Conf. on Robotics and Automation, ICRA’11
, 2011
"... Abstract — This paper proposes an adaptation of classical imagebased visual servoing to a generalised imaging model where cameras are modelled as sets of 3D viewing rays. This new model leads to a generalised visual servoing control formalism that can be applied to any type of imaging system whethe ..."
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Cited by 2 (0 self)
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Abstract — This paper proposes an adaptation of classical imagebased visual servoing to a generalised imaging model where cameras are modelled as sets of 3D viewing rays. This new model leads to a generalised visual servoing control formalism that can be applied to any type of imaging system whether it be multicamera, catadioptric, noncentral, etc. In this paper the generalised 3D viewing cones are parameterised geometrically via Plücker line coordinates. The new visual servoing model is tested on an atypical stereocamera system with nonoverlapping cameras. In this case no 3D information is available from triangulation and the system is comparable to a 2D visual servoing system with noncentral raybased control law. I.
RealTime 6D Stereo Visual Odometry with NonOverlapping Fields of View
"... In this paper, we present a framework for 6D absolute scale motion and structure estimation of a multicamera system in challenging indoor environments. It operates in realtime and employs information from two cameras with nonoverlapping fields of view. Monocular Visual Odometry supplying uptos ..."
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Cited by 1 (1 self)
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In this paper, we present a framework for 6D absolute scale motion and structure estimation of a multicamera system in challenging indoor environments. It operates in realtime and employs information from two cameras with nonoverlapping fields of view. Monocular Visual Odometry supplying uptoscale 6D motion information is carried out in each of the cameras, and the metric scale is recovered via a linear solution by imposing the known static transformation between both sensors. The redundancy in the motion estimates is finally exploited by a statistical fusion to an optimal 6D metric result. The proposed technique is robust to outliers and able to continuously deliver a reasonable measurement of the scale factor. The quality of the framework is demonstrated by a concise evaluation on indoor datasets, including a comparison to accurate ground truth data provided by an external motion tracking system. 1.
Absolute Scale in Structure from Motion from a Single Vehicle Mounted Camera
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.