Robust stereo ego-motion for long distance navigation

Olson, Clark F.; Matthies, Larry; Schoppers, H.; Maimone, Mark

doi:10.1109/cvpr.2000.854879

Cited by 112 publications

(74 citation statements)

References 8 publications

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“…When compared to monocular video, motion estimation from stereo images is relatively easy and tends to be more stable and well behaved [12]. Approaches for binocular motion estimation [13] typically involve establishing feature correspondences. The key steps are to 1.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection of Independent Motion using Stereo

Agrawal

Konolige

Iocchi

2005

2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1

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We describe a system that detects independently moving objects from a mobile platform in real time using a calibrated stereo camera. Interest points are first detected and tracked through the images. These tracks are used to obtain the motion of the platform by using an efficient threepoint algorithm in a RANSAC framework for outlier detection. We use a formulation based on disparity space for our inlier computation. In the disparity space, two disparity images of a rigid object are related by a homography that depends on the object's euclidean rigid motion. We use the homography obtained from the camera motion to detect the independently moving objects from the disparity maps obtained by an efficient stereo algorithm. Our system is able to reliably detect the independently moving objects at 16 Hz for a 320 x 240 stereo image sequence using a standard laptop computer.

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Section: Introductionmentioning

confidence: 99%

Real-Time Detection of Independent Motion using Stereo

Agrawal

Konolige

Iocchi

2005

2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1

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“…Instead, we use standard techniques in structure from motion to match image features and solve a projective system for the optimum local registration of frames and features [19][10] [25]. Our novel technique is to derive a synthetic nonlinear measurement among frames alone that summarizes the registration.…”

Section: Introductionmentioning

confidence: 99%

“…In visual odometry, the pose is estimated by matching image features across several consecutive frames [1][18] [19]. Current techniques achieve very precise results, but pose errors grow unbounded with time, even when the camera stays in the same area, because there is no matching of frames that are close in space, but not time.…”

Section: Introductionmentioning

confidence: 99%

Frame-Frame Matching for Realtime Consistent Visual Mapping

Konolige¹,

Agrawal²

2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

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Abstract. Many successful indoor mapping techniques employ frame-to-frame matching of laser scans to produce detailed local maps, as well as closing large loops. In this paper, we propose a framework for applying the same techniques to visual imagery, matching visual frames with large numbers of point features.The relationship between frames is kept as a nonlinear measurement, and can be used to solve large loop closures quickly. Both monocular (bearing-only) and binocular vision can be used to generate matches. Other advantages of our system are that no special landmark initialization is required, and large loops can be solved very quickly.

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“…Many feature tracking-based navigation methods exploit knowledge (either a priori, through binocular stereopsis, or by exploiting terrain homography) of the target location and solve the inverse trajectory projection problem [1], [10]. If no a priori knowledge of the scene is provided, egomotion estimation is completely correlated with estimating the scene.…”

mentioning

confidence: 99%

Stochastic Constraints for Fast Image Correspondence Search with Uncertain Terrain Model

Veth¹,

Raquet²,

Pachter³

2007

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Abstract-The navigation state (position, velocity, and attitude) can be determined using optical measurements from an imaging sensor pointed toward the ground. Extracting navigation information from an image sequence depends on tracking the location of stationary objects in multiple images, which is generally termed the correspondence problem. This is an active area of research and many algorithms exist which attempt to solve this problem by identifying a unique feature in one image and then searching subsequent images for a feature match. In general, the correspondence problem is plagued by feature ambiguity, temporal feature changes, and occlusions which are difficult for a computer to address. Constraining the correspondence search to a subset of the image plane has the dual advantage of increasing robustness by limiting false matches and improving search speed. A number of ad-hoc methods to constrain the correspondence search have been proposed in the literature.In this paper, a rigorous stochastic projection method is developed which constrains the correspondence search space by incorporating a priori knowledge of the aircraft navigation state using inertial measurements and a statistical terrain model. The stochastic projection algorithm is verified using Monte Carlo simulation and flight data. The constrained correspondence search area is shown to accurately predict the pixel location of a feature with an arbitrary level of confidence, thus promising improved speed and robustness of conventional algorithms.

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Robust stereo ego-motion for long distance navigation

Cited by 112 publications

References 8 publications

Real-Time Detection of Independent Motion using Stereo

Real-Time Detection of Independent Motion using Stereo

Frame-Frame Matching for Realtime Consistent Visual Mapping

Stochastic Constraints for Fast Image Correspondence Search with Uncertain Terrain Model

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