Showing papers by "Takeo Kanade published in 1986"

Adapting optical-flow to measure object motion in reflectance and X-ray image sequences

Abstract: : This paper adapts a previous work on optical flow to the problem of determining arbitrary motions of objects from 2-dimensional image sequences. The method allows for gradual changes in the way an object boundaries. The authors find velocity fields that give estimates of the velocities of objects in the image plane. These velocities are computed from a series of images using information about the spatial and temporal brightness gradients. A constraint on the smoothness of motion within an object's boundaries is used. The method can be applied to interpretation of both reflectance and x-ray images. Results are shown for models of ellipsoids undergoing expansion, as well as for an x-ray image sequence of a beating heart.

Progress in robot road-following

Abstract: We report progress in visual road following by autonomous robot vehicles. We present results and work in progress in the areas of system architecture, image rectification and camera calibration, oriented edge tracking, color classification and road-region segmentation, extracting geometric structure, and the use of a map. In test runs of an outdoor robot vehicle, the Terregator, under control of the Warp computer, we have demonstrated continuous motion vision-guided road-following at speeds up to 1.08 km/hour with image processing and steering servo loop times of 3 sec.

Autonomous land vehicle project at CMU

Abstract: 1 . Introduction This paper provides an overview of the Autonomous Land Vehicle (ALV) Project at CMU. The goal of the CMU ALV Project is to build vision and intelligence for a mobile robot capable of operating in the real world outdoors. We are attacking this on a number of fronts: building appropriate research vehicles, exploiting high. speed experimental computers, and building software for reasoning about the perceived world. Research topics includes: • Construction of research vehicles • Perception systems to perceive the natural outdoor scenes by means of multiple sensors including cameras (color, stereo, and motion), sonar sensors, and a 3D range finder • Path planning for obstacle avoidance Use of topological and terrain map • System architecture to facilitate the system integration • Utilization of parallel computer architectures Our current research vehicle is the Terregator built at CMU which is equipped with a sonar ring, a color camera, and the ERIM laser range finder. Its initial task is to follow roads and sidewalks in the park and on the campus, and avoid obstacles such as trees, humans, and traffic cones. 2. Vehicle, Sensors, and Host Computers The primary vehicle of the AMU ALV Project has been the Terregator, designed and built at CMU. The Terregator, short for Terrestrial Navigator, is designed to provide a clean separation between the vehicle itself and its sensor payload. As shown in

Real-time implementation and evaluation of model-based controls on CMU DD Arm II

Abstract: This paper presents the experimental results of the real-time performance of model-based control algorithms. We compare the computed-torque scheme which utilizes the complete dynamics model of the manipulator with the independent joint control scheme which assumes a decoupled and linear model of the manipulator dynamics. The two manipulator control schemes have been implemented on the CMU DD Arm II with a sampling period of 2 ms. Our initial investigation shows that the computed-torque scheme outperforms the independent joint control scheme as long as there is no torque saturation in the actuators.

Experimental Evaluation of the Feedforward Compensation and Computed-Torque Control Schemes

Abstract: This paper presents the experimental results of the real-time performance of model-based control algorithms. We compare the computed-torque control scheme with the feedforward dynamics compensation scheme. The feedforward scheme compensates for the manipulator dynamics in the feedforward path while the computed-torque scheme uses the dynamics in the feedback loop for linearization and decoupling. The manipulator control schemes have been implemented on the CMU DD Arm II with a sampling period of 2 ms.

Autonomous scene description with range imagery

Abstract: This paper presents a program to produce object-centered 3-dimensional descriptions starting from point-wise 3D range data obtained by a light-stripe rangefinder. A careful geometrical analysis shows that contours which appear in light-stripe range images can be classified into eight types, each with different characteristics in occluding vs occluded and different camera/illuminator relationships. Starting with detecting these contours in the iconic range image, the descriptions are generated moving up the hierarchy of contour, surface, object, to scene. We use conical and cylindrical surfaces as primitives. In this process, we exploit the fact that coherent relationships, such as symmetry, collinearity, and being coaxial, that are present among lower-level elements in the hierarchy allow us to hypothesize upper-level elements. The resultant descriptions are used for matching and recognizing objects. The analysis program has been applied to complex scenes containing cups, pans, and toy shovels. © 1985 Academic Press, Inc.

CMU strategic computing vision project report : 1984 to 1985

Abstract: This report describes work during the first year of CMU’s Strategic Computing Vision project. Our goal is to build an intelligent mobile robot capable of operating in the real world outdoors. We are approaching this problem by building experimental robot vehicles and software. Experiments in the first year have demonstrated vehicle guidance using sonar, stereo and monoscopic TV cameras, and a laser scanner. This report describes the technical contributions, our relationship with the OARPA Autonomous Land Vehicle project, our project history, the people who comprise our project, and a list of project publications over the last year.

Carnegie Mellon Navlab Vision System

Abstract: EDUCATION Ph.D. (9/98) Robotics, Carnegie Mellon University. Project: Automated Highway Systems, Navlab Thesis topic: A High-Performance Stereo Vision System for Obstacle Detection. Advisor: Dr. Charles Thorpe M.S. (5/94) Robotics, Carnegie Mellon University. Project: Unmanned Ground Vehicle (UGV), Navlab Advisor: Dr. Charles Thorpe B.S. (5/91) Applied Mathematics (Computer Science), Carnegie Mellon University B.S. (5/91) Physics, Carnegie Mellon University (with honors)

Incremental Reconstruction of 3D Scenes from Multiple Complex Images

Abstract: The 3D Mosaic system is a vision system that incrementally reconstructs complex 3D scenes from a sequence of images obtained from multiple viewpoints. The system encompasses several levels of the vision process, starting with images and ending with symbolic scene descriptions. This paper describes the various components of the system, including stereo analysis, monocular analysis, and constructing and updating the scene model. In addition, the representation of the scene model is described. This model is intended for tasks such as matching, display generation, planning paths through the scene, and making other decisions about the scene environment. Examples showing how the system is used to interpret complex aerial photographs of urban scenes are presented. Each view of the scene, which may be either a single image or a stereo pair, undergoes analysis which results in a 3D wire-frame description that represents portions of edges and vertices of objects. The model is a surface-based description constructed from the wire frames. With each successive view, the model is incrementally updated and gradually becomes more accurate and complete. Task-specific knowledge, involving block-shaped objects in an urban scene, is used to extract the wire frames and construct and update the model. The model is represented as a graph in terms of symbolic primitives such as faces, edges, vertices, and their topology and geometry. This permits the representation of partially complete, planar-faced objects. Because incremental modifications to the model must be easy to perform, the model contains mechanisms to (1) add primitives in a manner such that constraints on geometry imposed by these additions are propagated throughout the model, and (2) modify and delete primitives if discrepancies arise between newly derived and current information. The model also contains mechanisms that permit the generation, addition, and deletion of hypotheses for parts of the scene for which there is little data.