Mobile robot navigation and scene modeling using stereo fish-eye lens system

We present an autonomous mobile robot navigation system using stereo fish-eye lenses for navigation in an indoor structured environment and for generating a model of the imaged scene. The system estimates the three-dimensional (3D) position of significant features in the scene, and by estimating its relative position to the features, navigates through narrow passages and makes turns at corridor ends. Fish-eye lenses are used to provide a large field of view, which images objects close to the robot and helps in making smooth transitions in the direction of motion. Calibration is performed for the lens-camera setup and the distortion is corrected to obtain accurate quantitative measurements. A vision-based algorithm that uses the vanishing points of extracted segments from a scene in a few 3D orientations provides an accurate estimate of the robot orientation. This is used, in addition to 3D recovery via stereo correspondence, to maintain the robot motion in a purely translational path, as well as to remove the effects of any drifts from this path from each acquired image. Horizontal segments are used as a qualitative estimate of change in the motion direction and correspondence of vertical segment provides precise 3D information about objects close to the robot. Assuming detected linear edges in the scene as boundaries of planar surfaces, the 3D model of the scene is generated. The robot system is implemented and tested in a structured environment at our research center. Results from the robot navigation in real environments are presented and discussed. Received: 25 September 1996 / Accepted: 20 October 1996     

Head tracking using stereo

Head tracking is an important primitive for smart environments and perceptual user interfaces where the poses and movements of body parts need to be determined. Most previous solutions to this problem are based on intensity images and, as a result, suffer from a host of problems including sensitivity to background clutter and lighting variations. Our approach avoids these pitfalls by using stereo depth data together with a simple human-torso model to create a head-tracking system that is both fast and robust. We use stereo data (Commercial equipment and materials are identified in order to adequately specify certain procedures. In no case does such identification imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose.) to derive a depth model of the background that is then employed to provide accurate foreground segmentation. We then use directed local edge detectors on the foreground to find occluding edges that are used as features to fit to a torso model. Once we have the model parameters, the location and orientation of the head can be easily estimated. A useful side effect from using stereo data is the ability to track head movement through a room in three dimensions. Experimental results on real image sequences are given. Accepted: 13 August 2001     

A stereo-vision system for support of planetary surface exploration

Abstract. In this paper, we present a system that was developed for the European Space Agency (ESA) for the support of planetary exploration. The system that is sent to the planetary surface consists of a rover and a lander. The lander contains a stereo head equipped with a pan-tilt mechanism. This vision system is used both for modeling the terrain and for localization of the rover. Both tasks are necessary for the navigation of the rover. Due to the stress that occurs during the flight, a recalibration of the stereo-vision system is required once it is deployed on the planet. Practical limitations make it unfeasible to use a known calibration pattern for this purpose; therefore, a new calibration procedure had to be developed that could work on images of the planetary environment. This automatic procedure recovers the relative orientation of the cameras and the pan and tilt axes, as well as the exterior orientation for all the images. The same images are subsequently used to reconstruct the 3-D structure of the terrain. For this purpose, a dense stereo-matching algorithm is used that (after rectification) computes a disparity map. Finally, all the disparity maps are merged into a single digital terrain model. In this paper, a simple and elegant procedure is proposed that achieves that goal. The fact that the same images can be used for both calibration and 3-D reconstruction is important, since, in general, the communication bandwidth is very limited. In addition to navigation and path planning, the 3-D model of the terrain is also used for virtual-reality simulations of the mission, wherein the model is texture mapped with the original images. The system has been implemented, and the first tests on the ESA planetary terrain testbed were successful.     

Utilization of stereo disparity and optical flow information for the computer analysis of human interactions

We propose a system that simultaneously utilizes the stereo disparity and optical flow information of real-time stereo grayscale multiresolution images for the recognition of objects and gestures in human interactions. For real-time calculation of the disparity and optical flow information of a stereo image, the system first creates pyramid images using a Gaussian filter. The system then determines the disparity and optical flow of a low-density image and extracts attention regions in a high-density image. The three foremost regions are recognized using higher-order local autocorrelation features and linear discriminant analysis. As the recognition method is view based, the system can process the face and hand recognitions simultaneously in real time. The recognition features are independent of parallel translations, so the system can use unstable extractions from stereo depth information. We demonstrate that the system can discriminate the users, monitor the basic movements of the user, smoothly learn an object presented by users, and can communicate with users by hand signs learned in advance. Received: 31 January 2000 / Accepted: 1 May 2001 Correspondence to: I. Yoda (e-mail: yoda@ieee.org, Tel.: +81-298-615941, Fax: +81-298-613313)     

Multipass hierarchical stereo matching for generation of digital terrain models from aerial images

This paper presents a new multi-pass hierarchical stereo-matching approach for generation of digital terrain models (DTMs) from two overlapping aerial images. Our method consists of multiple passes which compute stereo matches with a coarse-to-fine and sparse-to-dense paradigm. An image pyramid is generated and used in the hierarchical stereo matching. Within each pass, the DTM is refined by using the image pyramid from the coarse to the fine level. At the coarsest level of the first pass, a global stereo-matching technique, the intra-/inter-scanline matching method, is used to generate a good initial DTM for the subsequent stereo matching. Thereafter, hierarchical block matching is applied to image locations where features are detected to refine the DTM incrementally. In the first pass, only the feature points near salient edge segments are considered in block matching. In the second pass, all the feature points are considered, and the DTM obtained from the first pass is used as the initial condition for local searching. For the passes after the second pass, 3D interactive manual editing can be incorporated into the automatic DTM refinement process whenever necessary. Experimental results have shown that our method can successfully provide accurate DTM from aerial images. The success of our approach and system has also been demonstrated with a flight simulation software. Received: 4 November 1996 / Accepted: 20 October 1997     

A memory-based approach to sensory-motor coordination

In this work a visual-based autonomous system capable of memorizing and recalling sensory-motor associations is presented. The robot's behaviors are based on learned associations between its sensory inputs and its motor actions. Perception is divided into two stages. The first one is functional: algorithmic procedures extract in real time visual features such as disparity and local orientation from the input images. The second stage is mnemonic: the features produced by the different functional areas are integrated with motor information and memorized or recalled. An efficient memory organization and fast information retrieval enables the robot to learn to navigate and to avoid obstacles without need of an internal metric reconstruction of the external environment. Received: 22 November 1996 / Accepted: 18 November 1997     

Tracking the human arm using constraint fusion and multiple-cue localization

Abstract. The use of hand gestures provides an attractive means of interacting naturally with a computer-generated display. Using one or more video cameras, the hand movements can potentially be interpreted as meaningful gestures. One key problem in building such an interface without a restricted setup is the ability to localize and track the human arm robustly in video sequences. This paper proposes a multiple-cue localization scheme combined with a tracking framework to reliably track the dynamics of the human arm in unconstrained environments. The localization scheme integrates the multiple cues of motion, shape, and color for locating a set of key image features. Using constraint fusion, these features are tracked by a modified extended Kalman filter that exploits the articulated structure of the human arm. Moreover, an interaction scheme between tracking and localization is used for improving the estimation process while reducing the computational requirements. The performance of the localization/tracking framework is validated with the help of extensive experiments and simulations. These experiments include tracking with calibrated stereo camera and uncalibrated broadcast video. Received: 19 January 2001 / Accepted: 27 December 2001 Correspondence to: R. Sharma     

A compact algorithm for rectification of stereo pairs

Abstract. We present a linear rectification algorithm for general, unconstrained stereo rigs. The algorithm takes the two perspective projection matrices of the original cameras, and computes a pair of rectifying projection matrices. It is compact (22-line MATLAB code) and easily reproducible. We report tests proving the correct behavior of our method, as well as the negligible decrease of the accuracy of 3D reconstruction performed from the rectified images directly. Received: 25 February 1999 / Accepted: 2 March 2000     

Real-time multiple vehicle detection and tracking from a moving vehicle

Abstract. A real-time vision system has been developed that analyzes color videos taken from a forward-looking video camera in a car driving on a highway. The system uses a combination of color, edge, and motion information to recognize and track the road boundaries, lane markings and other vehicles on the road. Cars are recognized by matching templates that are cropped from the input data online and by detecting highway scene features and evaluating how they relate to each other. Cars are also detected by temporal differencing and by tracking motion parameters that are typical for cars. The system recognizes and tracks road boundaries and lane markings using a recursive least-squares filter. Experimental results demonstrate robust, real-time car detection and tracking over thousands of image frames. The data includes video taken under difficult visibility conditions. Received: 1 September 1998 / Accepted: 22 February 2000     

Wearable Visual Robots

Research work reported in the literature in wearable visual computing has used exclusively static (or non-active) cameras, making the imagery and image measurements dependent on the wearer’s posture and motions. It is assumed that the camera is pointing in a good direction to view relevant parts of the scene at best by virtue of being mounted on the wearer’s head, or at worst wholly by chance. Even when pointing in roughly the correct direction, any visual processing relying on feature correspondence from a passive camera is made more difficult by the large, uncontrolled inter-image movements which occur when the wearer moves, or even breathes. This paper presents a wearable active visual sensor which is able to achieve a level of decoupling of camera movement from the wearer’s posture and motions by a combination of inertial and visual sensor feedback and active control. The issues of sensor placement, robot kinematics and their relation to wearability are discussed. The performance of the prototype robot is evaluated for some essential visual tasks. The paper also discusses potential applications for this kind of wearable robot.     

Reconstructing a textured CAD model of an urban environment using vehicle-borne laser range scanners and line cameras

Abstract. In this paper, a novel method is presented for generating a textured CAD model of an outdoor urban environment using a vehicle-borne sensor system. In data measurement, three single-row laser range scanners and six line cameras are mounted on a measurement vehicle, which has been equipped with a GPS/INS/Odometer-based navigation system. Laser range and line images are measured as the vehicle moves forward. They are synchronized with the navigation system so they can be geo-referenced to a world coordinate system. Generation of the CAD model is conducted in two steps. A geometric model is first generated using the geo-referenced laser range data, where urban features, such as buildings, ground surfaces, and trees are extracted in a hierarchical way. Different urban features are represented using different geometric primitives, such as a planar face, a triangulated irregular network (TIN), and a triangle. The texture of the urban features is generated by projecting and resampling line images onto the geometric model. An outdoor experiment is conducted, and a textured CAD model of a real urban environment is reconstructed in a full automatic mode.     

Range image segmentation using local approximation of scan lines with application to CAD model acquisition

Abstract. Automatic acquisition of CAD models from existing objects requires accurate extraction of geometric and topological information from the input data. This paper presents a range image segmentation method based on local approximation of scan lines. The method employs edge models that are capable of detecting noise pixels as well as position and orientation discontinuities of varying strengths. Region-based techniques are then used to achieve a complete segmentation. Finally, a geometric representation of the scene, in the form of a surface CAD model, is produced. Experimental results on a large number of real range images acquired by different range sensors demonstrate the efficiency and robustness of the method. Received: 1 August 2000 / Accepted: 23 January 2002 Correspondence to: I. Khalifa     

A bin picking system based on depth from defocus

It is generally accepted that to develop versatile bin-picking systems capable of grasping and manipulation operations, accurate 3-D information is required. To accomplish this goal, we have developed a fast and precise range sensor based on active depth from defocus (DFD). This sensor is used in conjunction with a three-component vision system, which is able to recognize and evaluate the attitude of 3-D objects. The first component performs scene segmentation using an edge-based approach. Since edges are used to detect the object boundaries, a key issue consists of improving the quality of edge detection. The second component attempts to recognize the object placed on the top of the object pile using a model-driven approach in which the segmented surfaces are compared with those stored in the model database. Finally, the attitude of the recognized object is evaluated using an eigenimage approach augmented with range data analysis. The full bin-picking system will be outlined, and a number of experimental results will be examined. Received: 2 December 2000 / Accepted: 9 September 2001 Correspondence to: O. Ghita     

Real-Time Active SLAM and Obstacle Avoidance for an Autonomous Robot Based on Stereo Vision

Abstract

In this article, the problem of real-time robot exploration and map building (active SLAM) is considered. A single stereo vision camera is exploited by a fully autonomous robot to navigate, localize itself, define its surroundings, and avoid any possible obstacle in the aim of maximizing the mapped region following the optimal route. A modified version of the so-called cognitive-based adaptive optimization algorithm is introduced for the robot to successfully complete its tasks in real time and avoid any local minima entrapment. The method’s effectiveness and performance were tested under various simulation environments as well as real unknown areas with the use of properly equipped robots.     

The perceptive workbench: Computer-vision-based gesture tracking,object tracking,and 3D reconstruction for augmented desks

Abstract. The Perceptive Workbench endeavors to create a spontaneous and unimpeded interface between the physical and virtual worlds. Its vision-based methods for interaction constitute an alternative to wired input devices and tethered tracking. Objects are recognized and tracked when placed on the display surface. By using multiple infrared light sources, the object's 3-D shape can be captured and inserted into the virtual interface. This ability permits spontaneity, since either preloaded objects or those objects selected at run-time by the user can become physical icons. Integrated into the same vision-based interface is the ability to identify 3-D hand position, pointing direction, and sweeping arm gestures. Such gestures can enhance selection, manipulation, and navigation tasks. The Perceptive Workbench has been used for a variety of applications, including augmented reality gaming and terrain navigation. This paper focuses on the techniques used in implementing the Perceptive Workbench and the system's performance.     

Vision-based 3-D trajectory tracking for unknown environments

This paper describes a vision-based system for 3-D localization of a mobile robot in a natural environment. The system includes a mountable head with three on-board charge-coupled device cameras that can be installed on the robot. The main emphasis of this paper is on the ability to estimate the motion of the robot independently from any prior scene knowledge, landmark, or extra sensory devices. Distinctive scene features are identified using a novel algorithm, and their 3-D locations are estimated with high accuracy by a stereo algorithm. Using new two-stage feature tracking and iterative motion estimation in a symbiotic manner, precise motion vectors are obtained. The 3-D positions of scene features and the robot are refined by a Kalman filtering approach with a complete error-propagation modeling scheme. Experimental results show that good tracking and localization can be achieved using the proposed vision system.     

Defect detection in textured surfaces using color ring-projection correlation

In this paper, we present a correlation scheme that incorporates a color ring-projection representation for the automatic inspection of defects in textured surfaces. The proposed color ring projection transforms a 2-D color image into a 1-D color pattern as a function of radius. For a search window of width W, data dimensionality is reduced from in the 2-D image to O(W) in the 1-D ring-projection space. The complexity of computing a correlation function is significantly reduced accordingly. Since the color ring-projection representation is invariant to rotation, the proposed method can be applied for both isotropic and oriented textures at arbitrary orientations. Experiments on regular textured surfaces have shown the efficacy of the proposed method. Received: 30 March 2000 / Accepted: 24 July 2001 Correspondence to: D.-M. Tsai (e-mail: iedmtsai@saturn.yzu.edu.tw)     

Active shape from stereo for highway inspection

Abstract. This paper describes an unsupervised algorithm for estimating the 3D profile of potholes in the highway surface, using structured illumination. Structured light is used to accelerate computation and to simplify the estimation of range. A low-resolution edge map is generated so that further processing may be focused on relevant regions of interest. Edge points in each region of interest are used to initialise open, active contour models, which are propagated and refined, via a pyramid, to a higher resolution. At each resolution, internal and external constraints are applied to a snake; the internal constraint is a smoothness function and the external one is a maximum-likelihood estimate of the grey-level response at the edge of each light stripe. Results of a provisional evaluation study indicate that this automated procedure provides estimates of pothole dimension suitable for use in a first, screening, assessment of highway condition. Received: 9 October 1998 / Accepted: 22 February 2000     

ATM layouts with bounded hop count and congestion

Summary. In this paper we introduce and analyze two new cost measures related to the communication overhead and the space requirements associated with virtual path layouts in ATM networks, that is the edge congestion and the node congestion. Informally, the edge congestion of a given edge e at an incident node u is defined as the number of VPs terminating at or starting from u and using e, while the node congestion of a node v is defined as the number of VPs having v as an endpoint. We investigate the problem of constructing virtual path layouts allowing to connect a specified root node to all the others in at most h hops and with maximum edge or node congestion c, for two given integers h and c. We first give tight results concerning the time complexity of the construction of such layouts for both the two congestion measures, that is we exactly determine all the tractable and intractable cases. Then, we provide some combinatorial bounds for arbitrary networks, together with optimal layouts for specific topologies such as chains, rings and grids. Received: December 1997 / Accepted: August 2000     

Automatic extraction of roads from aerial images based on scale space and snakes

Abstract. We propose a new approach for automatic road extraction from aerial imagery with a model and a strategy mainly based on the multi-scale detection of roads in combination with geometry-constrained edge extraction using snakes. A main advantage of our approach is, that it allows for the first time a bridging of shadows and partially occluded areas using the heavily disturbed evidence in the image. Additionally, it has only few parameters to be adjusted. The road network is constructed after extracting crossings with varying shape and topology. We show the feasibility of the approach not only by presenting reasonable results but also by evaluating them quantitatively based on ground truth. Received: 22 July 1999 / Accepted: 20 March 2000