Absolute motion and structure from stereo image sequences without stereo correspondence and analysis of degenerate cases

This paper deals with the estimation of motion and structure with an absolute scale factor from stereo image sequences without stereo correspondence. We show that the absolute motion and structure can be determined using only motion correspondences. This property is very useful in two aspects: first, motion correspondence is easier to solve than stereo correspondence because sequences of images can be taken at short time intervals; second, it is not necessary that the rigid scene be included in the intersection of the field of view of the two cameras. It is also shown that the degenerate cases reported in this paper constitute all of the degenerate cases for the scheme and can be easily avoided.     

Parametric ego-motion estimation for vehicle surround analysis using an omnidirectional camera

Omnidirectional cameras that give a 360° panoramic view of the surroundings have recently been used in many applications such as robotics, navigation, and surveillance. This paper describes the application of parametric ego-motion estimation for vehicle detection to perform surround analysis using an automobile-mounted camera. For this purpose, the parametric planar motion model is integrated with the transformations to compensate distortion in omnidirectional images. The framework is used to detect objects with independent motion or height above the road. Camera calibration as well as the approximate vehicle speed obtained from a CAN bus are integrated with the motion information from spatial and temporal gradients using a Bayesian approach. The approach is tested for various configurations of an automobile-mounted omni camera as well as a rectilinear camera. Successful detection and tracking of moving vehicles and generation of a surround map are demonstrated for application to intelligent driver support.Received: 1 August 2003, Accepted: 8 July 2004, Published online: 3 February 2005     

Improved Rover State Estimation in Challenging Terrain

Given ambitious mission objectives and long delay times between command-uplink/data-downlink sessions, increased autonomy is required for planetary rovers. Specifically, NASA's planned 2003 and 2005 Mars rover missions must incorporate increased autonomy if their desired mission goals are to be realized. Increased autonomy, including autonomous path planning and navigation to user designated goals, relies on good quality estimates of the rover's state, e.g., its position and orientation relative to some initial reference frame. The challenging terrain over which the rover will necessarily traverse tends to seriously degrade a dead-reckoned state estimate, given severe wheel slip and/or interaction with obstacles. In this paper, we present the implementation of a complete rover navigation system. First, the system is able to adaptively construct semi-sparse terrain maps based on the current ground texture and distances to possible nearby obstacles. Second, the rover is able to match successively constructed terrain maps to obtain a vision-based state estimate which can then be fused with wheel odometry to obtain a much improved state estimate. Finally the rover makes use of this state estimate to perform autonomous real-time path planning and navigation to user designated goals. Reactive obstacle avoidance is also implemented for roaming in an environment in the absence of a user designated goal. The system is demonstrated in soft soil and relatively dense rock fields, achieving state estimates that are significantly improved with respect to dead reckoning alone (e.g., 0.38 m mean absolute error vs. 1.34 m), and successfully navigating in multiple trials to user designated goals.     

星球漫游车超广角实时立体视觉系统

给出了一种用于星球漫游车障碍检测和定位的超大视场立体视觉系统的实现方法.该系统采用具有超广角镜头(对角视场角约160度)的双目或三目摄像机获取场景立体图像对,利用摄像机标定参数对大变形图像进行修正等预处理,然后在外极线、连续性等约束条件下,基于查找表和Intel MMx指令集,使用SAD算法快速进行对应点匹配计算.实验表明,该系统在图像分辨率为320×120像素、视差为64级时,利用普通工控机恢复稠密深度图的速度为10帧/秒,并能使机器人以1米/秒的速度行走.     

Speech recognition using a stereo vision neural network model

This paper describes a new algorithm for speech recognition by using stereo vision pattern recognition equations with competition and cooperation. In our research, we applied recently developed 3-layered neural net (3LNN) equations to speech recognition. Our proposed acoustic models using these equations yield better recognition results than the hidden Markov model (HMM). When using a 216 (240) word database, stereo vision acoustic models gave 6.5% (6.6%) higher accuracy than HMMs. This work was presented, in part, at the Fourth International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–22, 1999     

Real-time people localization and tracking through fixed stereo vision

Detecting, locating, and tracking people in a dynamic environment is important in many applications, ranging from security and environmental surveillance to assistance to people in domestic environments, to the analysis of human activities. To this end, several methods for tracking people have been developed in the field of Computer Vision using different settings, such as monocular cameras, stereo sensors, multiple cameras. In this article we describe a method for People Localization and Tracking (PLT) based on a calibrated fixed stereo vision sensor, its implementation and experimental results. The system analyzes three components of the stereo data (the left intensity image, the disparity image, and the 3-D world locations of measured points) to dynamically update a model of the background; extract foreground objects, such as people and rearranged furniture; track their positions in the world. The system is mostly suitable for indoor medium size environments. It can reliably detect and track people moving in an medium size area (a room or a corridor) in front of the sensor with high reliability and good precision.     

View-based navigation using an omniview sequence in a corridor environment

View- and appearance-based approaches have recently been attracting the interest of those involved in computer vision research. We have already proposed a visual view-based navigation method using a model of the route called the ”view sequence,” which contains a sequence of front views along a route memorized in the recording run. In this paper, we firstly apply the omnidirectional vision sensor to our view-based navigation method and propose an extended model of a route called the ”omniview sequence.” Secondly we propose a map named the ”view-sequenced map” which represents an entire corridor environment in a building. A method for the automatic acquisition of a view-sequenced map based on the exploration in a corridor using both stereo and omnidirectional vision is also described. Finally experimental results of the autonomous navigation and the map acquisition are presented to show the feasibility of the proposed methods. Correspondence to: Y. Matsumoto (e-mail: yoshio@is.aist-nara.ac.jp)     

Simultaneous estimation of ego-motion and vehicle distance by using a monocular camera

Auto navigation and vehicle distance estimation are of the critical importance in intelligent vehicles. This paper proposes a novel approach to solve these two problems simultaneously by using a monocular camera. First, we introduce an incremental depth parameterized model to overcome the scale ambiguity problem in monocular vision, which differs from other parameterized models with reduced state-dimension and consistent observability. In addition, the view-field of camera is divided into static and dynamic parts so that the ego- motion and vehicle-space can be calculated simultaneously. Outdoor experiments are implemented to validate the effectiveness of the proposed approach.     

Multi-object detection and tracking by stereo vision

This paper presents a new stereo vision-based model for multi-object detection and tracking in surveillance systems. Unlike most existing monocular camera-based systems, a stereo vision system is constructed in our model to overcome the problems of illumination variation, shadow interference, and object occlusion. In each frame, a sparse set of feature points are identified in the camera coordinate system, and then projected to the 2D ground plane. A kernel-based clustering algorithm is proposed to group the projected points according to their height values and locations on the plane. By producing clusters, the number, position, and orientation of objects in the surveillance scene can be determined for online multi-object detection and tracking. Experiments on both indoor and outdoor applications with complex scenes show the advantages of the proposed system.     

Three dimentional measurement of fish movement using stereo vision

Recently, the technology of cultivation has been and continuous to be developed in the fisheries and the studies for a high production efficiency are widely conducted. The reaction of fishes under the sound should be studied in order to develop the fish tank with less factors of stress. In this paper, three-dimensional measurement system of the fish motion has been presented in order to study the relation of the fishes under the stimulating sound. Three kinds of fishes are used to detect the reaction against the sounds. Frequency analysis is executed by using the threedimensional position data. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Depth extraction using a single moving camera: an integration of depth from motion and depth from stereo

An integrated approach to extract depth, efficiently and accurately, from a sequence of images is presented in this paper. The method combines the ability of the stereo processing to acquire highly accurate depth measurements and the efficiency of spatial and temporal gradient analysis. As a result of this integration, depth measurements of high quality are obtained at a speed approximately ten times greater than that of stereo processing. Without any a priori information of the locations of the points in the scene, the correspondence problem in stereo processing is computationally expensive. In our approach, we use spatial and temporal gradient (STG) analysis, which has been shown to provide depth with great efficiency, but limited accuracy, to guide the matching process of stereo. The camera motion used in the approach can be either lateral or axial. Extensive experiments on real scenes have shown the ability of the integrated approach to acquire depth with a mean error of less than 3%.     

Efficient contrast invariant stereo correspondence using dynamic programming with vertical constraint

In this paper, we propose a dense stereo algorithm based on the census transform and improved dynamic programming (DP). Traditional scanline-based DP algorithms are the most efficient ones among global algorithms, but are well-known to be affected by the streak effect. To solve this problem, we improve the traditional three-state DP algorithm by taking advantage of an extended version of sequential vertical consistency constraint. Using this method, we increase the accuracy of the disparity map greatly. Optimizations have been made so that the computational cost is only increased by about 20%, and the additional memory needed for the improvement is negligible. Experimental results show that our algorithm outperforms many state-of-the-art algorithms with similar efficiency on Middlebury College’s stereo Web site. Besides, the algorithm is robust enough for image pairs with utterly different contrasts by using of census transform as the basic match metric.     

Fast stereo matching using adaptive guided filtering

Dense disparity map is required by many great 3D applications. In this paper, a novel stereo matching algorithm is presented. The main contributions of this work are three-fold. Firstly, a new cost-volume filtering method is proposed. A novel concept named “two-level local adaptation” is introduced to guide the proposed filtering approach. Secondly, a novel post-processing method is proposed to handle both occlusions and textureless regions. Thirdly, a parallel algorithm is proposed to efficiently calculate an integral image on GPU, and it accelerates the whole cost-volume filtering process. The overall stereo matching algorithm generates the state-of-the-art results. At the time of submission, it ranks the 10th among about 152 algorithms on the Middlebury stereo evaluation benchmark, and takes the 1st place in all local methods. By implementing the entire algorithm on the NVIDIA Tesla C2050 GPU, it can achieve over 30 million disparity estimates per second (MDE/s).     

Tightly-coupled stereo vision-aided inertial navigation using feature-based motion sensors

A tightly-coupled stereo vision-aided inertial navigation system is proposed in this work, as a synergistic incorporation of vision with other sensors. In order to avoid loss of information possibly resulting by visual preprocessing, a set of feature-based motion sensors and an inertial measurement unit are directly fused together to estimate the vehicle state. Two alternative feature-based observation models are considered within the proposed fusion architecture. The first model uses the trifocal tensor to propagate feature points by homography, so as to express geometric constraints among three consecutive scenes. The second one is derived by using a rigid body motion model applied to three-dimensional (3D) reconstructed feature points. A kinematic model accounts for the vehicle motion, and a Sigma-Point Kalman filter is used to achieve a robust state estimation in the presence of non-linearities. The proposed formulation is derived for a general platform-independent 3D problem, and it is tested and demonstrated with a real dynamic indoor data-set alongside of a simulation experiment. Results show improved estimates than in the case of a classical visual odometry approach and of a loosely-coupled stereo vision-aided inertial navigation system, even in GPS (Global Positioning System)-denied conditions and when magnetometer measurements are not reliable.     

Optimal motion planning using navigation measure

We introduce navigation measure as a new tool to solve the motion planning problem in the presence of static obstacles. Existence of navigation measure guarantees collision-free convergence at the final destination set beginning with almost every initial condition with respect to the Lebesgue measure. Navigation measure can be viewed as a dual to the navigation function. While the navigation function has its minimum at the final destination set and peaks at the obstacle set, navigation measure takes the maximum value at the destination set and is zero at the obstacle set. A linear programming formalism is proposed for the construction of navigation measure. Set-oriented numerical methods are utilised to obtain finite dimensional approximation of this navigation measure. Application of the proposed navigation measure-based theoretical and computational framework is demonstrated for a motion planning problem in a complex fluid flow.     

3-D shape reconstruction in an active stereo vision system using genetic algorithms

The recovery of 3-D shape information (depth) using stereo vision analysis is one of the major areas in computer vision and has given rise to a great deal of literature in the recent past. The widely known stereo vision methods are the passive stereo vision approaches that use two cameras. Obtaining 3-D information involves the identification of the corresponding 2-D points between left and right images. Most existing methods tackle this matching task from singular points, i.e. finding points in both image planes with more or less the same neighborhood characteristics. One key problem we have to solve is that we are on the first instance unable to know a priori whether a point in the first image has a correspondence or not due to surface occlusion or simply because it has been projected out of the scope of the second camera. This makes the matching process very difficult and imposes a need of an a posteriori stage to remove false matching.In this paper we are concerned with the active stereo vision systems which offer an alternative to the passive stereo vision systems. In our system, a light projector that illuminates objects to be analyzed by a pyramid-shaped laser beam replaces one of the two cameras. The projections of laser rays on the objects are detected as spots in the image. In this particular case, only one image needs to be treated, and the stereo matching problem boils down to associating the laser rays and their corresponding real spots in the 2-D image. We have expressed this problem as a minimization of a global function that we propose to perform using Genetic Algorithms (GAs). We have implemented two different algorithms: in the first, GAs are performed after a deterministic search. In the second, data is partitioned into clusters and GAs are independently applied in each cluster. In our second contribution in this paper, we have described an efficient system calibration method. Experimental results are presented to illustrate the feasibility of our approach. The proposed method yields high accuracy 3-D reconstruction even for complex objects. We conclude that GAs can effectively be applied to this matching problem.     

双目立体匹配算法的研究与进展

立体匹配一直是计算机视觉领域的一个中心研究问题.首先综合介绍了立体匹配算法的研究概况,论述了双目立体匹配算法中各种约束的核心概念和适用范围;然后重点归纳分析了立体匹配算法的分类及其发展过程中的各种演化算法,对其关键技术进行了剖析和比较,并总结了目前存在的主要难题和可能的解决途径;最后对该领域存在的问题和技术发展趋势进行了分析和讨论.     

Accurate hardware-based stereo vision

To enable both accurate and fast real-time stereo vision in embedded systems, we propose a novel stereo matching algorithm that is designed for high efficiency when realized in hardware. We evaluate its accuracy using the Middlebury Stereo Evaluation, revealing its high performance at minimum tolerance. To outline the resource efficiency of the algorithm, we present its realization as an Intellectual Property (IP) core that is designed for the deployment in Field Programmable Gate Arrays (FPGAs) and Application Specific Integrated Circuits (ASICs).     

Evaluation of stereo correspondence algorithms and their implementation on FPGA

The accuracy of stereo vision has been considerably improved in the last decade, but real-time stereo matching is still a challenge for embedded systems where the limited resources do not permit fast operation of sophisticated approaches. This work presents an evaluation of area-based algorithms used for calculating distance in stereoscopic vision systems, their hardware architectures for implementation on FPGA and the cost of their accuracies in terms of FPGA hardware resources. The results show the trade-off between the quality of such maps and the hardware resources which each solution demands, so they serve as a guide for implementing stereo correspondence algorithms in real-time processing systems.     

Coarse-to-fine stereo vision with accurate 3D boundaries

This paper presents methods for efficient recovery of accurate binocular disparity estimates in the vicinity of 3D surface discontinuities. Of particular concern are methods that impact coarse-to-fine, local block-based matching as it forms the basis of the fastest and the most resource efficient stereo computation procedures. A novel coarse-to-fine refinement procedure that adapts match window support across scale to ameliorate corruption of disparity estimates near boundaries is presented. Extensions are included to account for half-occlusions and colour uniformity. Empirical results show that incorporation of these advances in the standard coarse-to-fine, block matching framework reduces disparity errors by more than a factor of two, while performing little extra computation, preserving low complexity and the parallel/pipeline nature of the framework. Moreover, the proposed advances prove to be beneficial for CTF global matchers as well.