Taylor, Camillo J
Now showing 1 - 10 of 35
PublicationOn the Optimal Assignment of Conference Papers to Reviewers(2008-01-01) Taylor, Camillo J PublicationTarget Tracking With Distributed Sensors: The Focus of Attention Problem(2003-01-01) Isler, Volkan; Khanna, Sanjeev; Spletzer, John; Taylor, Camillo JIn this paper, we investigate data fusion techniques for target tracking using distributed sensors. Specifically, we are interested in how pairs of bearing or range sensors can be best assigned to targets in order to minimize the expected error in the estimates. We refer to this as the focus of attention (FOA) problem. In its general form, FOA is NP-hard and not well approximable. However, for specific geometries we obtain significant approximation results: a 2-approximation algorithm for stereo cameras on a line, a PTAS for when the cameras are equidistant, and a 1.42 approximation for equally spaced range sensors on a circle. In addition to constrained geometries, we further investigate the problem for general sensor placement. By reposing as a maximization problem -- where the goal is to maximize the number of tracks with bounded error -- we are able to leverage results from maximum set-packing to render the problem approximable. We demonstrate these in simulation for a target tracking task, and for localizing a team of mobile agents in a sensor network. These results provide insights into sensor/target assignment strategies, as well as sensor placement in a distributed network. PublicationReconstruction of Linearly Parameterized Models from Single Images with a Camera of Unknown Focal Length(2001-07-01) Jelinek, David; Taylor, Camillo JThis paper deals with the problem of recovering the dimensions of an object and its pose from a single image acquired with a camera of unknown focal length. It is assumed that the object in question can be modeled as a polyhedron where the coordinates of the vertices can be expressed as a linear function of a dimension vector, λ. The reconstruction program takes as input, a set of correspondences between features in the model and features in the image. From this information, the program determines an appropriate projection model for the camera (scaled orthographic or perspective), the dimensions of the object, its pose relative to the camera and, in the case of perspective projection, the focal length of the camera. This paper describes how the reconstruction problem can be framed as an optimization over a compact set with low dimension - no more than four. This optimization problem can be solved efficiently by coupling standard nonlinear optimization techniques with a multistart method which generates multiple starting points for the optimizer by sampling the parameter space uniformly. The result is an efficient, reliable solution system that does not require initial estimates for any of the parameters being estimated. PublicationSensor Planning and Control in a Dynamic Environment(2002-05-01) Spletzer, John R; Taylor, Camillo JThis 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. We extend our previous work by showing how modeled system dynamics and configuration space obstacles can be handled. These 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. PublicationSensor based door navigation for a nonholonomic vehicle(2002-05-11) Patel, Sarangi; Jung, Sang-Hack; Ostrowski, James P.; Rao, Rahul; Taylor, Camillo JThis paper presents a sensor based algorithm for guiding a nonholonomic platform, such as a wheelchair, through a doorway. The controller uses information from a camera system and a laser range finder to perform image-based navigation. Simulations of the resultant switching controller are presented along with experimental results. A simple obstacle avoidance algorithm is also implemented on the experimental platform. Finally, we have considered the input of limited field-of-view constraints on this controller. All of these components together lead to a modal, image-based approach that will safely and robustly navigate a nonholonomic robot with sensor constraints through a doorway. PublicationAd Hoc Networks for Localization and Control(2002-12-10) Das, Aveek K.; Kumar, R. Vijay; Taylor, Camillo J; Spletzer, John RWe consider a team of mobile robots equipped with sensors and wireless network cards and the task of navigating to a desired location in a formation. We develop a set of algorithms for (a) discovery; (b) cooperative localization; and (c) cooperative control. Discovery involves the use of sensory information to organize the robots into a mobile network allowing each robot to establish its neighbors and, when necessary, one or more leaders. Cooperative control is the task of achieving a desired goal position and orientation and desired formation shape and maintaining it. Cooperative localization allows each robot to estimate its relative position and orientation with respect to its neighbors and hence the formation shape. We show numerical results and simulations for a team of nonholonomic, wheeled robots with omnidirectional cameras sharing a wireless communication network. PublicationSolving Stereo Matching Problems Using Interior Point Methods(2008-06-01) Taylor, Camillo J; Bhusnurmath, ArvindThis paper describes an approach to reformulating the stereo matching problem as a large scale Linear Program. The approach proceeds by approximating the match cost function associated with each pixel with a piecewise linear convex function. Regularization terms related to the first and second derivative of the disparity field are also captured with piecewise linear penalty terms. The resulting large scale linear program can be tackled using interior point methods and the associated Newton Steps involve matrices that reflect the structure of the underlying pixel grid. The proposed scheme effectively exploits the structure of these matrices to solve these linear systems efficiently. PublicationTowards Robotic Self-reassembly After Explosion(2007-10-29) Yim, Mark; Shirmohammadi, Babak; Sastra, Jimmy; Park, Michael; Taylor, Camillo J; Dugan, MichaelThis paper introduces a new challenge problem: designing robotic systems to recover after disassembly from high-energy events and a first implemented solution of a simplified problem. It uses vision-based localization for self-reassembly. The control architecture for the various states of the robot, from fully-assembled to the modes for sequential docking, are explained and inter-module communication details for the robotic system are described. PublicationA Framework and Architecture for Multi-Robot Coordination(2002-10-01) Alur, Rajeev; Das, Aveek J; Kumar, R. Vijay; Esposito, Joel; Lee, Insup; Fierro, Rafael; Grudic, Gregory; Pappas, George J; Hur, Yerang; Taylor, Camillo J; Ostrowski, James; Southall, B.; Spletzer, John RIn 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. PublicationTarget Tracking with Distributed Sensors: The Focus of Attention Problem(2003-10-27) Isler, Volkan; Khanna, Sanjeev; Spletzer, John R.; Taylor, Camillo JIn this paper, we investigate data fusion techniques for target tracking using distributed sensors. Specifically, we are interested in how pairs of bearing or range sensors can be best assigned to targets in order to minimize the expected error in the estimates. We refer to this as the focus of attention (FOA) problem. In its general form, FOA is NP-hard and not well approximable. However, for specific geometries we obtain significant approximation results: a 2-approximation algorithm for stereo cameras on a line, a PTAS for when the cameras are equidistant, and a 1.42 approximation for equally spaced range sensors on a circle. By reposing as a maximization problem - where the goal is to maximize the number of tracks with bounded error - we are able to leverage results from maximum set-packing to render the problem approximable. We demonstrate the results in simulation for a target tracking task, and for localizing a team of mobile agents in a sensor network. These results provide insights into sensor/target assignment strategies, as well as sensor placement in a distributed network.