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Dellaert, Frank

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search.filters.applied.f.isOrgUnitOfPublicationTitle: College of Computing × search.filters.applied.f.isOrgUnitOfPublicationTitle: Mobile Robot Laboratory ×

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Now showing 1 - 9 of 9
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Primate - Inspired Vehicle Navigation Using Optic Flow and Mental Rotations

2013 , Arkin, Ronald C. , Dellaert, Frank , Srinivasa, Natesh , Kerwin, Ryan

Robot navigation already has many relatively efficient solutions: reactive control, simultaneous localization and mapping (SLAM), Rapidly-Exploring Random Trees (RRTs), etc. But many primates possess an additional inherent spatial reasoning capability: mental rotation. Our research addresses the question of what role, if any, mental rotations can play in enhancing existing robot navigational capabilities. To answer this question we explore the use of optical flow as a basis for extracting abstract representations of the world, comparing these representations with a goal state of similar format and then iteratively providing a control signal to a robot to allow it to move in a direction consistent with achieving that goal state. We study a range of transformation methods to implement the mental rotation component of the architecture, including correlation and matching based on cognitive studies. We also include a discussion of how mental rotations may play a key role in understanding spatial advice giving, particularly from other members of the species, whether in map-based format, gestures, or other means of communication. Results to date are presented on our robotic platform.

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Efficient Particle Filter-Based Tracking of Multiple Interacting Targets Using an MRF-based Motion Model

2003 , Balch, Tucker , Dellaert, Frank , Khan, Zia

We describe a multiple hypothesis particle filter for tracking targets that will be influenced by the proximity and/or behavior of other targets. Our contribution is to show how a Markov random field motion prior, built on the fly at each time step, can model these interactions to enable more accurate tracking. We present results for a social insect tracking application, where we model the domain knowledge that two targets cannot occupy the same space, and targets will actively avoid collisions. We show that using this model improves track quality and efficiency. Unfortunately, the joint particle tracker we propose suffers from exponential complexity in the number of tracked targets. An approximation to the joint filter, however, consisting of multiple nearly independent particle filters can provide similar track quality at substantially lower computational cost.

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The Georgia Tech Yellow Jackets: A Marsupial Team for Urban Search and Rescue

2002 , Alegre, Fernando , Balch, Tucker , Berhault, Marc , Dellaert, Frank , Kaess, Michael , McGuire, Robert , Merrill, Ernest , Moshkina, Lilia , Ravichandran, Ram , Walker, Daniel

We describe our entry in the AAAI 2002 Urban Search and Rescue (USAR) competition, a marsupial team consisting of a larger wheeled robot and several small legged robots, carried around by the larger robot. This setup exploits complimentary strengths of each robot type in a challenging domain. We describe both the hardware and software architecture, and the on-board real-time mapping which forms the basis of accurate victim-localization crucial to the USAR domain. We also evaluate what challenges remain to be resolved in order to deploy search and rescue robots in realistic scenarios.

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Envisioning: Mental Rotation-based Semi-reactive Robot Control

2012 , Arkin, Ronald C. , Dellaert, Frank , Devassy, Joan

This paper describes ongoing research into the role of optic-flow derived spatial representations and their relation to cognitive computational models of mental rotation in primates, with the goal of producing effective and unique autonomous robot navigational capabilities. A theoretical framework is outlined based on a vectorial interlingua spanning perception, cognition and motor control. Progress to date on its implementation within an autonomous robot control architecture is presented.

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Marco Polo Localization

2003 , Dellaert, Frank , Martinson, Eric Beowulf

We introduce the Marco Polo Localization approach, where we apply sound as a tool for gathering range measurements between robots, and use those to solve a range-only Simultaneous Localization and Mapping problem. Range is calculated by correlating two recordings of the same sound, recorded on a pair of robots, after which the resulting time delay estimate is converted to a range measurement. The algorithmic approach we use is a straightforward application of the Bayesian estimation framework. We also present two complementary views on the associated optimization problem that provide insight into the problem and allows one to devise initialization strategies, indispensable in a range-only scenario. We illustrate the approach with both simulated and experimental results.

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Using the CONDENSATION Algorithm for Robust, Vision-Based Mobile Robot Localization

1999 , Burgard, Wolfram , Dellaert, Frank , Fox, Dieter , Thrun, Sebastian

To navigate reliably in indoor environments, a mobile robot must know where it is. This includes both the ability of globally localizing the robot from scratch, as well as tracking the robot’s position once its location is known. Vision has long been advertised as providing a solution to these problems, but we still lack efficient solutions in unmodified environments. Many existing approaches require modification of the environment to function properly, and those that work within unmodified environments seldomly address the problem of global localization. In this paper we present a novel, vision-based localization method based on the CONDENSATION algorithm [17, 18], a Bayesian filtering method that uses a sampling-based density representation. We show how the CONDENSATION algorithm can be used in a novel way to track the position of the camera platform rather than tracking an object in the scene. In addition, it can also be used to globally localize the camera platform, given a visual map of the environment. Based on these two observations, we present a vision-based robot localization method that provides a solution to a difficult and open problem in the mobile robotics community. As evidence for the viability of our approach, we show both global localization and tracking results in the context of a state of the art robotics application.

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Intrinsic Localization and Mapping With 2 Applications: Diffusion Mapping and Marco Polo Localization

2003 , Alegre, Fernando , Dellaert, Frank , Martinson, Eric Beowulf

We investigate Intrinsic Localization and Mapping (ILM) for teams of mobile robots, a multi-robot variant of SLAM where the robots themselves are used as landmarks. We develop what is essentially a straightforward application of Bayesian estimation to the problem, and present two complimentary views on the associated optimization problem that provide insight into the problem and allows one to devise initialization strategies, indispensable in practice. We also provide a discussion of the degrees of freedom and ambiguities in the solution. Finally, we introduce two applications of ILM that bring out its potential: Diffusion Mapping and Marco Polo localization.

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Linear 2D Localization and Mapping for Single and Multiple Robot Scenarios

2002 , Dellaert, Frank , Stroupe, Ashley W.

We show how to recover 2D structure and motion linearly in order to initialize Simultaneous Mapping and Localization (SLAM) for bearings-only measurements and planar motion. The method supplies a good initial estimate of the geometry, even without odometry or in multiple robot scenarios. Hence, it substantially enlarges the scope in which non-linear batch-type SLAM algorithms can be applied. The method is applicable when at least seven landmarks are seen from three different vantage points, whether by one robot that moves over time or by multiple robots that observe a set of common landmarks.

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Monte Carlo Localization for Mobile Robots

1999 , Burgard, Wolfram , Dellaert, Frank , Fox, Dieter , Thrun, Sebastian

To navigate reliably in indoor environments, a mobile robot must know where it is. Thus, reliable position estimation is a key problem in mobile robotics. We believe that probabilistic approaches are among the most promising candidates to providing a comprehensive and real-time solution to the robot localization problem. However, current methods still face considerable hurdles. In particular, the problems encountered are closely related to the type of representation used to represent probability densities over the robot’s state space. Recent work on Bayesian filtering with particle-based density representations opens up a new approach for mobile robot localization, based on these principles. In this paper we introduce the Monte Carlo Localization method, where we represent the probability density involved by maintaining a set of samples that are randomly drawn from it. By using a sampling-based representation we obtain a localization method that can represent arbitrary distributions. We show experimentally that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location. It is faster, more accurate and less memory-intensive than earlier grid-based methods.

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