3D information retrieval for visual odometry system of planetary exploration rovers - A stereo vision approach
21 Oct 2013-pp 354-360
TL;DR: This paper details the process flow and algorithm for the retrieval of 3-D information from the images obtained from the stereo rig, and the results obtained closely matches with ground truth.
Abstract: Determination of 3-D data from the images is important in the field of machine vision. The most direct way to achieve this is stereo vision. 3-D data obtained from stereo images form the basis for many higher level tasks like robot navigation, object recognition, 3-D modeling, path planning etc. in the field of interplanetary mission, hazardous environments, deep sea exploration, autonomous car driving. In this work, we attempt to recover the 3-D information from the stereo images for developing the vision based navigation of future planetary rover missions. This paper details the process flow and algorithm for the retrieval of 3-D information from the images obtained from our stereo rig. The results obtained in this work closely matches with ground truth.
Citations
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TL;DR: In this paper, a simple method to calibrate a stereo rig based on the backward projection process (BPP) is proposed, where the position of a spatial point can be determined separately from each camera by planar constraints provided by the planar pattern target.
Abstract: High-accuracy 3D measurement based on binocular vision system is heavily dependent on the accurate calibration of two rigidly-fixed cameras. In most traditional calibration methods, stereo parameters are iteratively optimized through the forward imaging process (FIP). However, the results can only guarantee the minimal 2D pixel errors, but not the minimal 3D reconstruction errors. To address this problem, a simple method to calibrate a stereo rig based on the backward projection process (BPP) is proposed. The position of a spatial point can be determined separately from each camera by planar constraints provided by the planar pattern target. Then combined with pre-defined spatial points, intrinsic and extrinsic parameters of the stereo-rig can be optimized by minimizing the total 3D errors of both left and right cameras. An extensive performance study for the method in the presence of image noise and lens distortions is implemented. Experiments conducted on synthetic and real data demonstrate the accuracy and robustness of the proposed method.
14 citations
01 Jul 2019
TL;DR: It is investigated that a common object held by a cooperative dual-arm system is made to perform a trajectory tracking control task in cartesian space under the monitoring of a calibrated binocular vision system, and the system stability is proven by Lyapunov method.
Abstract: It is investigated that a common object held by a cooperative dual-arm system is made to perform a trajectory tracking control task in cartesian space under the monitoring of a calibrated binocular vision system. To avoid the drawbacks of the position based visual servoing (PBVS), the image based visual servong (IBVS) is selected to do through transforming the control task in cartesian space to image space. The binocular visual trajectory tracking controller is then designed based on decentralised control strategy, and the system stability is proven by Lyapunov method. Finally, the effectiveness of the proposed method is verified in simulation.
Cites methods from "3D information retrieval for visual..."
...Therefore, in [11] stereo vision is then used to measure the position information of feature points on the object, and 3D reconstruction of the object is done with such position information....
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01 Jan 2015
TL;DR: Zhang et al. as discussed by the authors proposed a view-based stereo-model retrieval algorithm using a modified principal component analysis (PCA) algorithm to extract eigenvectors for each stereo model.
Abstract: With the development of computer vision technologies, manufacturing is now using stereo models for production, and the amount of stereo models generated recently has increased significantly. Thus, a fast and reliable model retrieval algorithm has become important. In this article, we propose a novel view-based stereo-model retrieval algorithm using a modified principal component analysis algorithm. Unlike a traditional principal component analysis that uses the origin of a two-dimensional image, we apply principal component analysis to extract eigenvectors for each stereo model. First, we extract a set of two-dimensional images from different directions of the stereo object. Because each two-dimensional image can be seen as one sample of the stereo object, we utilize principal component analysis to extract the eigenvectors as a dictionary for each stereo object. Then, those eigenvectors are used to rebuild the query. Finally, the reconstruction residual is applied to represent the similarity between the query and the candidate stereo object. Experimentally, the proposed retrieval algorithm has been evaluated using the ETH-80 and ALOI datasets. Experimental results and comparisons with other methods show the effectiveness of the proposed approach that can be used in engineering manufacturing and computer-aided design applications.
02 Mar 2015
TL;DR: A novel view-based stereo-model retrieval algorithm using a modified principal component analysis algorithm that can be used in engineering manufacturing and computer-aided design applications.
Abstract: With the development of computer vision technologies, manufacturing is now using stereo models for production, and the amount of stereo models generated recently has increased significantly. Thus, a fast and reliable model retrieval algorithm has become important. In this article, we propose a novel view-based stereo-model retrieval algorithm using a modified principal component analysis algorithm. Unlike a traditional principal component analysis that uses the origin of a two-dimensional image, we apply principal component analysis to extract eigenvectors for each stereo model. First, we extract a set of two-dimensional images from different directions of the stereo object. Because each two-dimensional image can be seen as one sample of the stereo object, we utilize principal component analysis to extract the eigenvectors as a dictionary for each stereo object. Then, those eigenvectors are used to rebuild the query. Finally, the reconstruction residual is applied to represent the similarity between the q...
References
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Book•
01 Jan 2000TL;DR: In this article, the authors provide comprehensive background material and explain how to apply the methods and implement the algorithms directly in a unified framework, including geometric principles and how to represent objects algebraically so they can be computed and applied.
Abstract: From the Publisher:
A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Recent major developments in the theory and practice of scene reconstruction are described in detail in a unified framework. The book covers the geometric principles and how to represent objects algebraically so they can be computed and applied. The authors provide comprehensive background material and explain how to apply the methods and implement the algorithms directly.
15,558 citations
01 Jan 2001
TL;DR: This book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts and it will show the best book collections and completed collections.
Abstract: Downloading the book in this website lists can give you more advantages. It will show you the best book collections and completed collections. So many books can be found in this website. So, this is not only this multiple view geometry in computer vision. However, this book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts. This is simple, read the soft file of the book and you get it.
14,282 citations
01 Jan 1988
TL;DR: The problem the authors are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work.
Abstract: The problem we are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work. For example, we desire to obtain an understanding of natural scenes, containing roads, buildings, trees, bushes, etc., as typified by the two frames from a sequence illustrated in Figure 1. The solution to this problem that we are pursuing is to use a computer vision system based upon motion analysis of a monocular image sequence from a mobile camera. By extraction and tracking of image features, representations of the 3D analogues of these features can be constructed.
13,993 citations
Proceedings Article•
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TL;DR: A system that estimates the motion of a stereo head or a single moving camera based on video input in real-time with low delay and the motion estimates are used for navigational purposes.
Abstract: We present a system that estimates the motion of a stereo head or a single moving camera based on video input. The system operates in real-time with low delay and the motion estimates are used for navigational purposes. The front end of the system is a feature tracker. Point features are matched between pairs of frames and linked into image trajectories at video rate. Robust estimates of the camera motion are then produced from the feature tracks using a geometric hypothesize-and-test architecture. This generates what we call visual odometry, i.e. motion estimates from visual input alone. No prior knowledge of the scene nor the motion is necessary. The visual odometry can also be used in conjunction with information from other sources such as GPS, inertia sensors, wheel encoders, etc. The pose estimation method has been applied successfully to video from aerial, automotive and handheld platforms. We focus on results with an autonomous ground vehicle. We give examples of camera trajectories estimated purely from images over previously unseen distances and periods of time.
1,786 citations
Book•
01 Jan 1980
TL;DR: The Stanford AI Lab cart as discussed by the authors is a card-table sized mobile robot controlled remotely through a radio link, and equipped with a TV camera and transmitter equipped with an onboard TV system.
Abstract: : The Stanford AI Lab cart is a card-table sized mobile robot controlled remotely through a radio link, and equipped with a TV camera and transmitter A computer has been programmed to drive the cart through cluttered indoor and outdoor spaces, gaining its knowledge of the world entirely from images broadcast by the onboard TV system The cart uses several kinds of stereo to locate objects around it in 3D and to deduce its own motion It plans an obstacle avoiding path to a desired destination on the basis of a model built with this information The plan changes as the cart perceives new obstacles on its journey The system is reliable for short runs, but slow The cart moves one meter every ten to fifteen minutes, in lurches After rolling a meter it stops, takes some pictures and thinks about them for a long time Then it plans a new path, executes a little of it, and pauses again The program has successfully driven the cart through several 20 meter indoor courses (each taking about five hours) complex enough to necessitate three or four avoiding swerves A less successful outdoor run, in which the cart skirted two obstacles but collided with a third, was also done Harsh lighting (very bright surfaces next to very dark shadows) giving poor pictures and movement of shadows during the cart's creeping progress were major reasons for the poorer outdoor performance The action portions of these runs were filmed by computer controlled cameras (Author)
1,050 citations
"3D information retrieval for visual..." refers background in this paper
...Moreover, due to the uneven nature of the terrain, the position information from wheel odometry will be erroneous....
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