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59
Iterative point matching for registration of freeform curves and surfaces
, 1994
"... A heuristic method has been developed for registering two sets of 3D curves obtained by using an edgebased stereo system, or two dense 3D maps obtained by using a correlationbased stereo system. Geometric matching in general is a difficult unsolved problem in computer vision. Fortunately, in ma ..."
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Cited by 499 (6 self)
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A heuristic method has been developed for registering two sets of 3D curves obtained by using an edgebased stereo system, or two dense 3D maps obtained by using a correlationbased stereo system. Geometric matching in general is a difficult unsolved problem in computer vision. Fortunately, in many practical applications, some a priori knowledge exists which considerably simplifies the problem. In visual navigation, for example, the motion between successive positions is usually approximately known. From this initial estimate, our algorithm computes observer motion with very good precision, which is required for environment modeling (e.g., building a Digital Elevation Map). Objects are represented by a set of 3D points, which are considered as the samples of a surface. No constraint is imposed on the form of the objects. The proposed algorithm is based on iteratively matching points in one set to the closest points in the other. A statistical method based on the distance distribution is used to deal with outliers, occlusion, appearance and disappearance, which allows us to do subsetsubset matching. A leastsquares technique is used to estimate 3D motion from the point correspondences, which reduces the average distance between points in the two sets. Both synthetic and real data have been used to test the algorithm, and the results show that it is efficient and robust, and yields an accurate motion estimate.
The Computation of Optical Flow
, 1995
"... Twodimensional image motion is the projection of the threedimensional motion of objects, relative to a visual sensor, onto its image plane. Sequences of timeordered images allow the estimation of projected twodimensional image motion as either instantaneous image velocities or discrete image dis ..."
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Cited by 224 (10 self)
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Twodimensional image motion is the projection of the threedimensional motion of objects, relative to a visual sensor, onto its image plane. Sequences of timeordered images allow the estimation of projected twodimensional image motion as either instantaneous image velocities or discrete image displacements. These are usually called the optical flow field or the image velocity field. Provided that optical flow is a reliable approximation to twodimensional image motion, it may then be used to recover the threedimensional motion of the visual sensor (to within a scale factor) and the threedimensional surface structure (shape or relative depth) through assumptions concerning the structure of the optical flow field, the threedimensional environment and the motion of the sensor. Optical flow may also be used to perform motion detection, object segmentation, timetocollision and focus of expansion calculations, motion compensated encoding and stereo disparity measurement. We investiga...
A Review of Statistical Data Association Techniques for Motion Correspondence
 International Journal of Computer Vision
, 1993
"... Motion correspondence is a fundamental problem in computer vision and many other disciplines. This article describes statistical data association techniques originally developed in the context of target tracking and surveillance and now beginning to be used in dynamic motion analysis by the computer ..."
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Cited by 121 (3 self)
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Motion correspondence is a fundamental problem in computer vision and many other disciplines. This article describes statistical data association techniques originally developed in the context of target tracking and surveillance and now beginning to be used in dynamic motion analysis by the computer vision community. The Mahalanobis distance measure is first introduced before discussing the limitations of nearest neighbor algorithms. Then, the tracksplitting, joint likelihood, multiple hypothesis algorithms are described, each method solving an increasingly more complicated optimization. Realtime constraints may prohibit the application of these optimal methods. The suboptimal joint probabilistic data association algorithm is therefore described. The advantages, limitations, and relationships between the approaches are discussed. 1
An Efficient Implementation and Evaluation of Reid's Multiple Hypothesis Tracking Algorithm for Visual Tracking
, 1994
"... An efficient implementation of Reid's multiple hypothesis tracking (MHT) algorithm is presented in which the the kbest hypotheses are determined in polynomial time using an algorithm due to Murty [13]. The MHT algorithm is then applied to several motion sequences. The MHT capabilities of track ..."
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Cited by 33 (1 self)
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An efficient implementation of Reid's multiple hypothesis tracking (MHT) algorithm is presented in which the the kbest hypotheses are determined in polynomial time using an algorithm due to Murty [13]. The MHT algorithm is then applied to several motion sequences. The MHT capabilities of track initiation, termination and continuation are demonstrated. Continuation allows the MHT to function despite temporary occlusion of tracks. Between 50 and 150 corner features are simultaneously tracked in the image plane over a sequence of up to 60 frames. Each corner is tracked using a simple linear Kalman filter and any data association uncertainty is resolved by the MHT. Kalman filter parameter estimation is discussed and experimental results show that the algorithm is robust to errors in the motion model. 1 Introduction The analysis of image sequences for purposes of estimating camera motion and/or 3D scene geometry often requires the tracking of geometric features over long image sequences....
A Factorization Method for Affine Structure from Line Correspondences
 In Proceedings of the CVPR
, 1996
"... A family of structure from motion algorithms called the factorization method has been recently developed from the orthographic projection model to the affine camera model [23, 16, 18]. All these algorithms are limited to handling only point features of the image stream. We propose in this paper an a ..."
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Cited by 25 (2 self)
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A family of structure from motion algorithms called the factorization method has been recently developed from the orthographic projection model to the affine camera model [23, 16, 18]. All these algorithms are limited to handling only point features of the image stream. We propose in this paper an algorithm for the recovery of shape and motion from line correspondences by the factorization method with the affine camera. Instead of one step factorization for points, a multistep factorization method is developed for lines based on the decomposition of the whole shape and motion into three separate substructures. Each of these substructures can then be linearly solved by factorizing the appropriate measurement matrices. It is also established that affine shape and motion with uncalibrated affine cameras can be achieved with at least seven lines over three views, which extends the previous results of Koenderink and Van Doorn [9] for points to lines. 1 Introduction Points and line segmen...
Token Tracking in a Cluttered Scene
 Image and Vision Computing
, 1993
"... The statistical data association technique is an important approach to analyze long sequences of images in Computer Vision. Although it has extensively been studied in other domains such as in radar imagery, it was introduced only recently in Computer Vision, and is already recognized as an efficien ..."
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Cited by 21 (0 self)
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The statistical data association technique is an important approach to analyze long sequences of images in Computer Vision. Although it has extensively been studied in other domains such as in radar imagery, it was introduced only recently in Computer Vision, and is already recognized as an efficient approach to solving correspondence and motion problems. This paper has two purposes. The first is to present a general formulation of token tracking. The parameterization of tokens is not addressed. This might be useful to those who are not familiar with statistical tracking techniques. The second is to introduce some strategies for tracking with emphasis on practical importance. They include beam search for resolving multiple matches, support of existence for discarding false matches, and locking on reliable tokens and maximizing local rigidity for handling combinatorial explosion. We have implemented those strategies in a 3D line segment tracking algorithm and found them very useful.
3D Motion and Structure from 2D Motion Causally Integrated over Time: Implementation
 In IEEE Trans. Robotics and Automation
, 2000
"... The causal estimation of threedimensional motion from a sequence of twodimensional images can be posed as a nonlinear filtering problem. We describe the implementation of an algorithm whose uniform observability, minimal realization and stability have been proven analytically in [5]. We discuss a ..."
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Cited by 20 (1 self)
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The causal estimation of threedimensional motion from a sequence of twodimensional images can be posed as a nonlinear filtering problem. We describe the implementation of an algorithm whose uniform observability, minimal realization and stability have been proven analytically in [5]. We discuss a scheme for handling occlusions, drift in the scale factor and tuning of the lter. We also present an extension to partially calibrated camera models and prove its observability. We report the performance of our implementation on a few long sequences of real images. More importantly, however, we have made our realtime implementation  which runs on a personal computer  available to the public for firsthand testing.
Recursive Estimation of TimeVarying Motion and Structure Parameters
, 1995
"... We present a computational framework for recovering both 1 st order motion parameters (observer direction of translation and observer rotation), 2 nd order motion parameters (observer rotational acceleration) and relative depth maps from timevarying optical flow. We recover translation speed ..."
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Cited by 18 (7 self)
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We present a computational framework for recovering both 1 st order motion parameters (observer direction of translation and observer rotation), 2 nd order motion parameters (observer rotational acceleration) and relative depth maps from timevarying optical flow. We recover translation speed and acceleration in units which are scaled relative to the distance to the object. Our assumption is that the observer rotational motion is no more than "second order"; in other words, observer motion is either constant or has at most constant acceleration. We examine the effect of noise on the solution of the motion and structure parameters. This ensemble of unknowns comprises a solution to the classical `structureandmotion from optic flow' problem. Our complete framework utilizes a method for interpreting the bilinear image velocity equation by solving simple systems of linear equations. Since our noise analysis yields uncertainty measures for each parameter, a Kalman filter is employed...
LargeScale 6DOF SLAM With StereoinHand
 IEEE TRANSACTIONS ON ROBOTICS
, 2008
"... In this paper, we describe a system that can carry out simultaneous localization and mapping (SLAM) in large indoor and outdoor environments using a stereo pair moving with 6 DOF as the only sensor. Unlike current visual SLAM systems that use either bearingonly monocular information or 3D stereo ..."
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Cited by 15 (0 self)
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In this paper, we describe a system that can carry out simultaneous localization and mapping (SLAM) in large indoor and outdoor environments using a stereo pair moving with 6 DOF as the only sensor. Unlike current visual SLAM systems that use either bearingonly monocular information or 3D stereo information, our system accommodates both monocular and stereo. Textured point features are extracted from the images and stored as 3D points if seen in both images with sufficient disparity, or stored as inverse depth points otherwise. This allows the system to map both near and far features: the first provide distance and orientation, and the second provide orientation information. Unlike other visiononly SLAM systems, stereo does not suffer from “scale drift ” because of unobservability problems, and thus, no other information such as gyroscopes or accelerometers is required in our system. Our SLAM algorithm generates sequences of conditionally independent local maps that can share information related to the camera motion and common features being tracked. The system computes the full map using the novel conditionally independent divide and conquer algorithm, which allows constant time operation most of the time, with linear time updates to compute the full map. To demonstrate the robustness and scalability of our
Large scale 6DOF SLAM with stereoinhand
 IEEE TRANSACTIONS ON ROBOTICS
, 2008
"... In this paper we describe a system that can carry out SLAM in large indoor and outdoor environments using a stereo pair moving with 6DOF as the only sensor. Unlike current visual SLAM systems that use either bearingonly monocular information or 3D stereo information, our system accommodates both mo ..."
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Cited by 14 (0 self)
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In this paper we describe a system that can carry out SLAM in large indoor and outdoor environments using a stereo pair moving with 6DOF as the only sensor. Unlike current visual SLAM systems that use either bearingonly monocular information or 3D stereo information, our system accommodates both monocular and stereo. Textured point features are extracted from the images and stored as 3D points if seen in both images with sufficient disparity, or stored as inverse depth points otherwise. This allows the system to map both near and far features: the first provide distance and orientation, and the second orientation information. Unlike other vision only SLAM systems, stereo does not suffer from ’scale drift’ because of unobservability problems, and thus no other information such as gyroscopes or accelerometers is required in our system. Our SLAM algorithm generates sequences of conditionally independent local maps that can share information related to the camera motion and common features being tracked. The system computes the full map using the novel Conditionally Independent Divide and Conquer algorithm, which allows constant time operation most of the time, with linear time updates to compute the full map. To demonstrate the robustness and scalability of our system, we show experimental results in indoor and outdoor urban environments of 210m and 140m loop trajectories, with the stereo camera being carried in hand by a person walking at normal walking speeds of 4 − 5km/hour.