Stereo Vision Method Research Articles

Development of an Environment Recognition System for Multiple Mobile Robots Controlled by a Human

This paper deals with an environmental recognition method for operation of multiple mobile robots. Until now. we have been developing a system for multiple mobile robot operation by a single human user. In order that the user operates multiple robots appropriately. he/she must know information of an environment around the robots. Hence we build a virtual environment and show it to the user. To build the virtual environment. we must solve two problems; object recognition and self-positioning. Concerning the former. the two robots. each equipped with a monocular camera, acquire a 3-dimensional position of a point within an environment with stereovision method. As for the latter. markers are placed on the robots so that their relative positions can be measured. In each phase mentioned above, the human user gives commands to the robots appropriately. After the problems mentioned above are solved. the virtual environment can be constructed with the environmental information. The virtual environment displays remote site information to the user. Experiment shows the effectiveness of the proposed method.

Fast block matching algorithm based on the winner-update strategy

Block matching is a widely used method for stereo vision, visual tracking, and video compression. Many fast algorithms for block matching have been proposed in the past, but most of them do not guarantee that the match found is the globally optimal match in a search range. This paper presents a new fast algorithm based on the winner-update strategy which utilizes an ascending lower bound list of the matching error to determine the temporary winner. Two lower bound lists derived by using partial distance and by using Minkowski's inequality are described. The basic idea of the winner-update strategy is to avoid, at each search position, the costly computation of the matching error when there exists a lower bound larger than the global minimum matching error. The proposed algorithm can significantly speed up the computation of the block matching because: 1) computational cost of the lower bound we use is less than that of the matching error itself; 2) an element in the ascending lower bound list will be calculated only when its preceding element has already been smaller than the minimum matching error computed so far; 3) for many search positions, only the first several lower bounds in the list need to be calculated. Our experiments have shown that, when applying to motion vector estimation for several widely-used test videos, 92% to 98% of operations can be saved while still guaranteeing the global optimality. Moreover, the proposed algorithm can be easily modified either to meet the limited time requirement or to provide an ordered list of best candidate matches. Our source codes of the proposed algorithm are available at http://smart.iis.sinica.edu.tw/html/winup.html.

Self-calibration of a biplane X-ray imaging system for an optimal three dimensional reconstruction

The purpose of this paper is to elaborate a three dimensional (3D) reconstruction method, using biplane X-ray angiograms acquired daily by the clinician, without any special calibration procedure during the X-ray examination. The absolute geometry of the X-ray imaging system is determined by an iterative procedure based on the minimization of the mean square distance between observed and analytical projections of a set of reference points identified by the clinician on the simultaneous pair of images. Once the geometry of the imaging system is found the 3D structure of interest is retrieved from classical methods of binocular stereovision. This 3D information is a prerequisite for an accurate evaluation of the degree of severity of a vascular structure or motion anomaly and therefore, for establishing an appropriate diagnosis. The proposed 3D reconstruction method is validated on synthetic and real data and is shown to perform robustly and accurately in the presence of noise. The method should be particularly useful in clinical applications as it needs very little intervention from the clinician.

Automatic generation of high resolution urban zone digital elevation models

Our paper presents an automatic generation of high resolution urban digital elevation models (DEMs) based on a highly redundant correlation process. We will discuss the difficulties of such a task by commenting on the state of the art, and we propose an approach in three main steps. In the first step, the image acquisition specification as image sequences leads to pairs with various base/height ratios in order to obtain good precision and few errors due to hidden parts. In the second step we use various stereovision methods and we merge the results, thus attributing to each pixel the most probable and precise elevation. In the third step we automatically extract terrain-DEM and building-DEM from computed DEM in order to specifically post-process each class. Finally, we combine these two DEMs to generate a final DEM which presents the best continuity for ground surface, and which respects sharp building discontinuities. The results obtained with an operational example (including image size, difficulty of the scene) demonstrate the feasibility of generating metric resolution urban data bases from automated digital stereo methods.

A stereo vision-based obstacle detecting method for mobile robot navigation

SUMMARYThis paper presents a computationally simple stereo vision method for detecting useful features of obstacles on the ground relevant to a mobile robot's navigation in an indoor environment. Enhanced time efficiency and reliability is achieved by introducing a geometrical image transformation and a frame-wise iterative edge image comparison scheme. The image transformation used in this paper relates each constant disparity value to an oblique plane at an elevation from the floor. The stereo correspondence method is devised by implementing a frame-wise AND operation based upon edge orientation and a subsequent pixel wise intensity correlation checking. This method is efficient in terms of computation time and capable of identifying isodisparity points, so that the height information of all the obstacle points above the floor can be determined. The obtained disparities are recorded on a local map to give complete obstacle features for mobile robot's path planning. Through a series of experiments performed under various environmental conditions, it is found that the proposed method can effectively be applied for locating obstacles of various heights in indoor navigation of mobile robots.

A new passive measurementmethod by trinocular stereo vision

In the present paper, a new trinocular stereo vision method is proposed. After abrief survey of binocular stereo vision methods, some essential limitations are indicated. A quantitative description of the false target problem in stereo vision is proposed and applied to binocular and trinocular stereo vision. It is shown that a new trinocular stereo vision strategy matching the feature primitives in 3-D space can effectively solve the false targets. The new method for matching trinocular images is then presented. A new calibration method is also proposed and we show that all linear cameras can be transformed into an equilvalent orthonormal one and both the intrinsic and extrinsic parameters of a trinocular stereo vision system can be calibrated without using any 3-D coordinates, which provides a passive measurement system with an auto-calibration capability. A system based on the proposed methods has been realized and tested for computer-generated and real images. The experimental results are satisfactory. Erroneous matching is always less than 1% and the precision for 3-D reconstruction is 2 mm at a distance of about 1.5 m.

Vision for a Robot Moving in an Indoor Environment

This paper presents four complementary research contributions used to provide a representation of the 3D indoor environment of a mobile robot. The first tool needed to produce adequate information is the segmentation algorithm: we choose to perform a region segmentation according to a region growing method. The perception of the distance is done by using two cameras: we have implemented a stereovision method founded on region matching to reconstruct 3D surfaces. We have developed a monocular analysis to extract the perspective information from the images. Finally, parts of all this information need to be structured in order to be able to use themThe result of all this work is a rich collection of partly structured 2D and 3D information, which represents the first step towards model learning so that the robot may be able to locate itself to navigate and to identify the objects surrounding itselfThe four processes developed are inter-dependent; for example, the structuration uses the vanishing points extracted by monocular analysis and the 3D segments produced by stereovision depend on the segmentation results

A 3-D Sensor System for Teaching Robot Paths and Environments

In this paper, we discuss a three-dimensional (3-D) sensor system that can simultaneously measure 3-D pose ( position and orientation) of robot environments. The 3-D sensor system consists of a camera using a position-sensitive device and multiple LEDs ( light emitting diode) attached to robotic devices, such as a robot's hand and a teaching device. 3-D pose of these devices is calculated in real time based on both two-dimensional coordinate values of the LEDs in an image plane of the camera and geometrical relationships among 3-D positions of the LEDs. The 3-D sensor system has the advantages of simplicity and practical usefulness for obtain ing 3-D information about robotic devices at high speed. We describe the experimental results of applications to visual detection of a robot's hand position and orientation, teaching of robot paths with a robot language system, and modeling of robot environments using the stereo-vision method.

Obstacle Detection Using Stereo WithoutCorrespondence

Stereo vision is a straightforward approach for 3D information perception. However image correspondence is the most difficulty in real-time applications. In this paper a qualitative 3D scene verification method is proposed. The global correspondence problem is simplified to an optimization for disparity map parameters. The results of fitting provide the camera pose parameters. The obstacles are then detected from the abnormality of disparity. As we known, simple two-dimensional vision based road following methods are not sufficient for Autonomous Land Vehicle (ALV) navigation in complex environments under arbitrary weather and illumination conditions. They can not recognize 3D obstacles efficiently, and may detect the shadow or water on the road as false obstacles. In this paper the novel qualitative stereo-vision method we proposed is designed for real-time obstacle detection on structural roads. The optimal disparity map of the image pair can be easily computed, and it is a linear function on the image plane. Road scene is verified by the optimal disparity map. A morphology procedure is applied for more reliable abnormal-disparity region extraction. These regions are the focus-of-attention parts for following processing, as obstacle avoidance or object recognition. The algorithm is not correspondence related, so it is very efficient and can be implemented in real-time. Experiments show that this approach is simple and robust. Transactions on Information and Communications Technologies vol 16, © 1996 WIT Press, www.witpress.com, ISSN 1743-3517