Robust 6DOF Motion Estimation for Non-Overlapping, Multi-Camera Systems

Clipp, Brian; Kim, Jae-Hak; Frahm, Jan‐Michael; Pollefeys, Marc; Hartley, Richard

doi:10.1109/wacv.2008.4544011

Cited by 64 publications

(63 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This can be done by using single cameras (perspective or omnidirectional) [2,10], stereo cameras [6], or multi-camera systems [1]. The advantage of using more than one camera is that both the motion and the 3D structure can be computed directly in the absolute scale when the distance between the cameras is known.…”

Section: Introductionmentioning

confidence: 99%

“…The advantage of using more than one camera is that both the motion and the 3D structure can be computed directly in the absolute scale when the distance between the cameras is known. Furthermore, the cameras not necessarily need to have an overlapping field of view, as shown in [1]. Conversely, when using a single camera the absolute scale must be computed L (a) (b) Figure 1.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Absolute scale in structure from motion from a single vehicle mounted camera by exploiting nonholonomic constraints

Scaramuzza

Fraundorfer

Pollefeys

et al. 2009

2009 IEEE 12th International Conference on Computer Vision

Self Cite

111

View full text Add to dashboard Cite

In structure-from-motion 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 structure-from-motion 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 moves. We give a mathematical derivation and provide experimental results on both simulated and real data. 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.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Absolute scale in structure from motion from a single vehicle mounted camera by exploiting nonholonomic constraints

Scaramuzza

Fraundorfer

Pollefeys

et al. 2009

2009 IEEE 12th International Conference on Computer Vision

Self Cite

111

View full text Add to dashboard Cite

show abstract

“…Features seen in only the left or right camera at both poses (two-view features) are labeled S 1.. 3 and the feature seen in all four camera views is labeled Q(four-view feature). Please note that within this paper we refer to 2D image matches as correspondences and 3D features or their projections as features.…”

Section: Introductionmentioning

confidence: 99%

“…Another approach was presented by Clipp et al [3]. They solved the same problem using the minimal six point feature correspondences, five in one camera to determine the rotation and translation direction of the multiple camera system and the sixth in another camera of the multi-camera system which determines the scale.…”

Section: Introductionmentioning

confidence: 99%

“…They solved the same problem using the minimal six point feature correspondences, five in one camera to determine the rotation and translation direction of the multiple camera system and the sixth in another camera of the multi-camera system which determines the scale. Both of these methods [3,8] are based on decomposing the translation of the systems's cameras into translation of the total system and the rotation induced translation. This can only be done in special circumstances when these two vectors are not parallel or close to parallel.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A new minimal solution to the relative pose of a calibrated stereo camera with small field of view overlap

Clipp

Zach

Frahm

et al. 2009

2009 IEEE 12th International Conference on Computer Vision

Self Cite

View full text Add to dashboard Cite

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 real-time structure from motion system.

show abstract

Expanding the Limits of Vision‐based Localization for Long‐term Route‐following Autonomy

Paton

Pomerleau

MacTavish

et al. 2016

Journal of Field Robotics

View full text Add to dashboard Cite

Vision‐based, autonomous, route‐following algorithms enable robots to autonomously repeat manually driven routes over long distances. Through the use of inexpensive, commercial vision sensors, these algorithms have the potential to enable robotic applications across multiple industries. However, in order to extend these algorithms to long‐term autonomy, they must be able to operate over long periods of time. This poses a difficult challenge for vision‐based systems in unstructured and outdoor environments, where appearance is highly variable. While many techniques have been developed to perform localization across extreme appearance change, most are not suitable or untested for vision‐in‐the‐loop systems such as autonomous route following, which requires continuous metric localization to keep the robot driving. In this paper, we present a vision‐based, autonomous, route‐following algorithm that combines multiple channels of information during localization to increase robustness against daily appearance change such as lighting. We explore this multichannel visual teach and repeat framework by adding the following channels of information to the basic single‐camera, gray‐scale, localization pipeline: images that are resistant to lighting change and images from additional stereo cameras to increase the algorithm's field of view. Using these methods, we demonstrate robustness against appearance change through extensive field deployments spanning over 26 km with an autonomy rate greater than 99.9%. We furthermore discuss the limits of this system when subjected to harsh environmental conditions by investigating keypoint match degradation through time.

show abstract

Robust 6DOF Motion Estimation for Non-Overlapping, Multi-Camera Systems

Abstract: Abstract

Cited by 64 publications

References 14 publications

Absolute scale in structure from motion from a single vehicle mounted camera by exploiting nonholonomic constraints

Absolute scale in structure from motion from a single vehicle mounted camera by exploiting nonholonomic constraints

A new minimal solution to the relative pose of a calibrated stereo camera with small field of view overlap

Expanding the Limits of Vision‐based Localization for Long‐term Route‐following Autonomy

Contact Info

Product

Resources

About