14456844
Exploration strategies for mobile robots
The problem of programming a robot to carry out a systematic exploration of its environment using realistic sensors is considered. The robot is modeled as a single point moving in a two-dimensional configuration space populated with visually opaque and transparent obstacles. The robot is equipped with proximity sensors, a vision-based recognition system, and a method of odometry, all of which have some uncertainty associated with their measurements. By using visually distinctive configurations of features in the world as natural landmarks, a series of local maps is constructed. The geometrical relationships between mutually visible landmarks are used to build a relational map from this collection of local maps. This representation forms the basis for a systematic exploration algorithm. The approach is implemented in simulation, and results are presented.<<ETX>>
1926044
Real-time vision-based control of a nonholonomic mobile robot
This paper considers the problem of vision-based control of a nonholonomic mobile robot. We describe the design and implementation of real-time estimation and control algorithms on a car-like robot platform using a single omni-directional camera as a sensor without explicit use of odometry. We provide experimental results for each of these vision-based control objects. The algorithms are packaged as control modes and can be combined hierarchically to perform higher level tasks involving multiple robots.
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.
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.
2497525
Structure and Motion from Line Segments in Multiple Images
This paper presents a new method for recovering the three dimensional structure of a scene composed of straight line segments using the image data obtained from a moving camera. The recovery algorithm is formulated in terms of an objective function which measures the total squared distance in the image plane between the observed edge segments and the projections (perspective) of the reconstructed lines. This objective function is minimized with respect to the line parameters and the camera positions to obtain an estimate for the structure of the scene. The effectiveness of this approach is demonstrated quantitatively through extensive simulations and qualitatively with actual image sequences. The implementation is being made publicly available. >
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.
2293701
Automatically building appearance models from image sequences using salient features
Abstract We address the problem of automatically placing landmarks across an image sequence to define correspondences between frames. The marked up sequence is then used to build a statistical model of the appearance of the object within the sequence. We locate the most salient object features from within the first frame and attempt to track them throughout the sequence. Salient features are those which have a low probability of being misclassified as any other feature, and are therefore more likely to be robustly tracked throughout the sequence. The method automatically builds statistical models of the objects shape and the salient features appearance as the sequence is tracked. These models are used in subsequent frames to further improve the probability of finding accurate matches. Results are shown for several face image sequences. The quality of the model is comparable with that generated from hand labelled images.
12495173
Interactions Between Learning and Evolution in Simulated Avian Communication
This paper presents a computational framework for studying the influence of learning on the evolution of avian communication. We conducted computer simulations for exploring the effects of different learning strategies on the evolution of bird song. Experimental results show the genetic assimilation of song repertoires as a consequence of interactions between learning and evolution.
18409496
Building optimal 2D statistical shape models
Statistical shape models are used widely as a basis for segmenting and interpreting images. A major drawback of the approach is the need, during training, to establish a dense correspondence across a training set of segmented shapes. We show that model construction can be treated as an optimisation problem, automating the process and guaranteeing the effectiveness of the resulting models. This is achieved by optimising an objective function with respect to the correspondence. We use an information theoretic objective function that directly promotes desirable features of the model. This is coupled with an effective method of manipulating correspondence, based on reparameterising each training shape, to build optimal statistical shape models. The method is evaluated on several training sets of shapes, showing that it constructs better models than alternative approaches. q 2003 Elsevier B.V. All rights reserved.
33520001
Active Appearance Models
We describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations in the model parameters and the induced image errors.
15389117
A Framework for Automatic Landmark Identification Using a New Method of Nonrigid Correspondence
A framework for automatic landmark identification is presented based on an algorithm for corresponding the boundaries of two shapes. The auto-landmarking framework employs a binary tree of corresponded pairs of shapes to generate landmarks automatically on each of a set of example shapes. The landmarks are used to train statistical shape models, known as point distribution models. The correspondence algorithm locates a matching pair of sparse polygonal approximations, one for each of a pair of boundaries by minimizing a cost function, using a greedy algorithm. The cost function expresses the dissimilarity in both the shape and representation error (with respect to the defining boundary) of the sparse polygons. Results are presented for three classes of shape which exhibit various types of nonrigid deformation.