Differential epipolar constraint in mobile robot egomotion estimation

Armangué, Xavier; Araújo, Hélder; Salví, Joaquím

doi:10.1109/icpr.2002.1048010

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…[4]), where as the monocular approach accomplishes this using sequential images and evaluating the optical flow. The monocular approach additionally requires the knowledge about the ego-motion of the camera which can be obtained either by an inertial measurement unit (IMU) or by the optical flow itself [1].…”

Section: Tracking Of Image Featuresmentioning

confidence: 99%

Evaluation of moving object segmentation comparing 6D-vision and monocular motion constraints

Vaudrey

Wedel

Rabe

et al. 2008

2008 23rd International Conference Image and Vision Computing New Zealand

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Detecting moving objects is a very important aspect of driver assistance systems (DAS). This paper handles this issue by using a vision based system mounted within the vehicle. The pipeline for both a stereoscopic and monocular approach are covered. Both approaches use image sequences and compare moving feature points over time. This sparse information is then segmented using the optimal graph-cut algorithm, by also incorporating the grey-scale images. This paper then evaluates and contrasts the two approaches to identify the accuracy and robustness of each approach. The two methods both work in real-time on normal PC hardware (Quad Core CPU).Motion is one of the major cues for human perception. Detecting moving objects is also a major issue for driver assistance systems (DAS) and road safety. The authors consider the detection of moving traffic participants to be an important step toward attention-based environment perception. This paper investigates methods and limitations of both monocular and binocular camera systems for motion detectability. It is evident that a monocular system is cheaper, uses less installation space, and suffers less decalibration issues, compared to a stereo system. However, a stereo system yields direct range measurement estimates (e.g. [5]), but the orientation between the two cameras needs to be known accurately, and decalibration can cause major issues. This paper provides insight into the difference between monocular and stereo camera performance.The motion of the ego-vehicle greatly complicates the problem of motion detection because simple background subtraction of successive images yields no result. The key idea behind our approach of detecting independently moving objects is to distinguish between motion in the images caused by the ego-motion of the ego-vehicle (static objects) and motion caused by dynamic objects in the scene. This paper presents and investigates techniques to distinguish between stationary and non-stationary 978-1-4244-2582-2/08/$25.00 c 2008 IEEE points. They are based on tracking feature points in sequential images.Section 2 presents our algorithm, starting with an investigation of motion analysis techniques, and followed by presenting segmentation of the moving objects from the static scene. In Section 3, different scenarios are presented and analysed, confirming the practicality of computer vision for the sensation and perception of motion. Differences between monocular and binocular motion detection are discussed and segmentation results for moving objects are presented. The concluding section is on future work and obtained insights. Our AlgorithmThe proposed algorithm is able to find both rigid objects such as cars and non-rigid objects such as moving pedestrians, and it is subdivided into two main steps:Step 1. As a first result, feature points on independently moving objects are detected as moving. These features, however, are sparse and do not characterize the whole image.Step 2. In a second step, moving objects are segmented in the images using these...

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Section: Tracking Of Image Featuresmentioning

confidence: 99%

Evaluation of moving object segmentation comparing 6D-vision and monocular motion constraints

Vaudrey

Wedel

Rabe

et al. 2008

2008 23rd International Conference Image and Vision Computing New Zealand

View full text Add to dashboard Cite

show abstract

“…The stereoscopic approach accomplishes this using a pair of stereo images by estimating the disparity and using triangulation, where as the monocular approach accomplishes this using sequential images and evaluating the optical flow. The monocular approach additionally requires the knowledge about the ego-motion of the camera which can be obtained either by an inertial measurement unit (IMU) [7] or based on optical flow [2,14].…”

Section: Motion Analysismentioning

confidence: 99%

Moving Object Segmentation Using Optical Flow and Depth Information

Klappstein

Vaudrey

Rabe

et al. 2009

Lecture Notes in Computer Science

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This paper discusses the detection of moving objects (being a crucial part of driver assistance systems) using monocular or stereoscopic computer vision. In both cases, object detection is based on motion analysis of individually tracked image points (optical flow), providing a motion metric which corresponds to the likelihood that the tracked point is moving. Based on this metric, points are segmented into objects by employing a globally optimal graph-cut algorithm. Both approaches are comparatively evaluated using real-world vehicle image sequences.

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“…However, in the minimization of these epipolar errors it is necessary to find a way of avoiding local minima that are dependent on an initialization value. In comparison, although methods using differential epipolar constraints [11,12] are able to robustly estimate the camera motion from image sequences, they cannot simultaneously recover camera motion and three-dimensional structure from omnidirectional image sequences.…”

Section: Introductionmentioning

confidence: 97%

Planar motion and three‐dimensional structure recovery from image sequences based on a fish‐eye projection model

Miyagawa

Wakabayashi

Arakawa

2006

Systems & Computers in Japan

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SUMMARYIn this paper we propose a factorization method that can simultaneously recover planar motion and three-dimensional structure from image sequences captured using a fish-eye camera. Here we assume that the fish-eye camera moves on a plane with the optical axis perpendicular to the plane. Our method is based on a fish-eye projection model that captures the image sequence under this planar motion. In our experiments using an imaging robot, we apply this method to images obtained indoors and show that it is an effective method for simultaneously recovering planar motion and three-dimensional structure with a high level of accuracy. In addition, from evaluations of the precision of shape recovery we confirm that the precision with which shapes can be recovered is dependent on the angle of elevation and thereby clarify the scope within which this method is applicable.

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Differential epipolar constraint in mobile robot egomotion estimation

Cited by 6 publications

References 9 publications

Evaluation of moving object segmentation comparing 6D-vision and monocular motion constraints

Evaluation of moving object segmentation comparing 6D-vision and monocular motion constraints

Moving Object Segmentation Using Optical Flow and Depth Information

Planar motion and three‐dimensional structure recovery from image sequences based on a fish‐eye projection model

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