110260194
A Comparative Study of Vision-Based Lateral Control Strategies for Autonomous Highway Driving
With the increasing speeds of modern microprocessors, it has become ever more common for computer-vision algorithms to find application in real-time control tasks. In this paper, we present an analysis of the problem of steering an autonomous vehicle along a highway based on the images obtained from a CCD camera mounted in the vehicle. We explore the effects of changing various important system parameters like the vehicle velocity, the look-ahead range of the vision sensor, and the processing delay associated with the perception and control systems. We also present the results of a series of experiments that were designed to provide a systematic comparison of a number of control strategies. The control strategies that were explored include a lead-lag control law, a full-state linear controller, and an input-output linearizing control law. Each of these control strategies was implemented and tested at highway speeds on our experimental vehicle platform, a Honda Accord LX sedan.
8867506
Robust vision-based pose control
The problem of controlling the spatial position and orientation of a robotic platform based on the image data obtained from a video camera mounted on that platform is considered. More specifically, we propose control laws that will cause the robot to achieve and maintain a fixed position and orientation with respect to a set of feature points in the scene. We demonstrate analytically that the proposed control scheme is globally convergent even in the presence of large calibration errors in both the intrinsic parameters of the camera and in the extrinsic parameters which relate the frame of reference of the camera to the body frame of the robot platform which is being controlled. Furthermore, no a priori knowledge about the structure of the scene is assumed.
15498523
A framework for sensor planning and control with applications to vision guided multi-robot systems
The paper presents an approach to the problem of controlling the configuration of a team of mobile agents equipped with cameras so as to optimize the quality of the estimates derived from their measurements. The issue of optimizing the robots' configuration is particularly important in the context of teams equipped with vision sensors since most estimation schemes of interest will involve some form of triangulation. We provide a theoretical framework for tackling the sensor planning problem and a practical computational strategy, inspired by work on particle filtering, for implementing the approach. The ideas have been demonstrated both in simulation and on actual robotic platforms. The results indicate that the framework is able to solve fairly difficult sensor planning problems online without requiring excessive amounts of computational resources.
21050185
A Framework and Architecture for Multirobot Coordination
In this paper, we present a framework and the software architecture for the deployment of multiple autonomous robots in an unstructured and unknown environment with applications ranging from scouting and reconnaissance, to search and rescue and manipulation tasks. Our software framework provides the methodology and the tools that enable robots to exhibit deliberative and reactive behaviors in autonomous operation, to be reprogrammed by a human operator at run-time, and to learn and adapt to unstructured, dynamic environments and new tasks, while providing performance guarantees. We demonstrate the algorithms and software on an experimental testbed that involves a team of car-like robots using a single omnidirectional camera as a sensor without explicit use of odometry.
2609415
Modeling and rendering architecture from photographs: a hybrid geometry- and image-based approach
We present a new approach for modeling and rendering existing architectural scenes from a sparse set of still photographs. Our modeling approach, which combines both geometry-based and imagebased techniques, has two components. The first component is a photogrammetricmodeling method which facilitates the recovery of the basic geometry of the photographed scene. Our photogrammetric modeling approach is effective, convenient, and robust because it exploits the constraints that are characteristic of architectural scenes. The second component is a model-based stereo algorithm, which recovers how the real scene deviates from the basic model. By making use of the model, our stereo technique robustly recovers accurate depth from widely-spaced image pairs. Consequently, our approach can model large architectural environments with far fewer photographs than current image-based modeling approaches. For producing renderings, we present view-dependent texture mapping, a method of compositing multiple views of a scene that better simulates geometric detail on basic models. Our approach can be used to recover models for use in either geometry-based or image-based rendering systems. We present results that demonstrate our approach’s ability to create realistic renderings of architectural scenes from viewpoints far from the original photographs. CR Descriptors: I.2.10 [Artificial Intelligence]: Vision and Scene Understanding Modeling and recovery of physical attributes; I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism Color, shading, shadowing, and texture I.4.8 [Image Processing]: Scene Analysis Stereo; J.6 [Computer-Aided Engineering]: Computer-aided design (CAD).
16474707
Reconstructing Polyhedral Models of Architectural Scenes from Photographs
This paper presents a new image-based modeling method that facilitates the recovery of accurate polyhedral models of architectural scenes. The method is particularly effective because it exploits many of the constraints that are characteristic of architectural scenes. This work is placed in the context of the FaCade project, whose goal is to use images to produce photo-realistic novel views of architectural scenes.
1277199
Modeling and minimization of interconnect energy dissipation in nanometer technologies
As the technology sizes of semiconductor devices continue to decrease, the effect of nanometer technologies on interconnects, such as crosstalk glitches and timing variations, become more significant. In this paper, we study the effect of nanometer technologies on energy dissipation in interconnects. We propose a new power estimation technique which considers DSM effects, resulting in significantly more accurate energy dissipation estimates than transition-count based methods for on-chip interconnects. We also introduce an energy minimization technique which attempts to minimize large voltage swings across the cross-coupling capacitances between interconnects. Even though the number of transitions may increase, our method yields a decrease in power consumption of up to 50%.
13161464
Macroscopic traffic flow modelling and ramp metering control using Matlab/Simulink
Computer programs to simulate traffic flow offer an opportunity to evaluate new strategies for reducing delays, congestion, fuel consumption and pollution. This paper describes a Statistical Traffic Model or STM, which is based on accepted macro-modelling concepts, such as the conservation of vehicles and the fundamental traffic diagram. In this case, the model is constructed using the well known Matlab/Simulink™ software package, so providing an integrated approach for data processing, graphical presentation of data, control system design and macroscopic simulation in one straightforward to use, widely available environment. To illustrate the methodology, the STM is applied to a section of the M3/M27 Ramp Metering Pilot Scheme in the UK. This Highways Agency sponsored project, based in the Southampton area, utilises traffic lights at the on-ramp entrances to regulate access to the main carriageway of the motorway, in an attempt to maintain flow close to the capacity. The paper utilises the model to help design a locally-coordinated ramp metering algorithm, based on proportional-integral-plus (PIP) control methods. In this manner, the STM proves particularly valuable for the application of multi-objective optimisation techniques in the design of new traffic management systems.
16065751
Improving Appearance Model Matching Using Local Image Structure
We show how non-linear representations of local image structure can be used to improve the performance of model matching algorithms in medical image analysis tasks. Rather than represent the image structure using intensity values or gradients, we use measures that indicate the reliability of a set of local image feature detector outputs. These features are image edges, corners, and gradients. Feature detector outputs in flat, noisy regions tend to be ignored whereas those near strong structure are favoured. We demonstrate that combinations of these features give more accurate and reliable matching between models and new images than modelling image intensity alone. We also show that the approach is robust to non-linear changes in contrast, such as those found in multi-modal imaging.
35329802
Shape Discrimination in the Hippocampus Using an MDL Model
We extend recent work on building 3D statistical shape models, automatically, from sets of training shapes and describe an application in shape analysis. Using an existing measure of model quality, based on a minimum description length criterion, and an existing method of surface re-parameterisation, we introduce a new approach to model optimisation that is scalable, more accurate, and involves fewer parameters than previous methods. We use the new approach to build a model of the right hippocampus, using a training set of 82 shapes, manually segmented from 3D MR images of the brain. We compare the results with those obtained using another previously published method for building 3D models, and show that our approach results in a model that is significantly more specific, general, and compact. The two models are used to investigate the hypothesis that there are differences in hippocampal shape between age-matched schizophrenic and normal control subgroups within the training set. Linear discriminant analysis is used to find the combination of shape parameters that best separates the two subgroups. We perform an unbiased test that shows there is a statistically significant shape difference using either shape model, but that the difference is more significant using the model built using our approach. We show also that the difference between the two subgroups can be visualised as a mode of shape variation.
35191926
Automatic Extraction of the Face Identity-Subspace
Facial variation divides into a number of functional subspaces, and ensemblespecific variation. An improved method of measuring these is presented, within the space defined by an Appearance Model. Initial estimates of the subspaces (lighting, pose, identity and expression) are obtained by Principal Components Analysis on appropriate groups of faces. An expectationmaximization algorithm is applied to image codings to maximise the probability of coding across these non-orthogonal subspaces. Ensemble specific variation is then removed by measuring the spatial predictability of the eigenvectors excluding those which are less predictable than the ensemble. These procedures significantly enhance identity recognition for a disjoint test set.