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» Monte Carlo Localization Using SIFT Features
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89
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ICRA
2003
IEEE
158views Robotics» more  ICRA 2003»
15 years 2 months ago
Probabilistic cooperative localization and mapping in practice
In this paper we present a probabilistic framework for the reduction in the uncertainty of a moving robot pose during exploration by using a second robot to assist. A Monte Carlo ...
Ioannis M. Rekleitis, Gregory Dudek, Evangelos E. ...
96
Voted
SSPR
2004
Springer
15 years 3 months ago
An MCMC Feature Selection Technique for Characterizing and Classifying Spatial Region Data
We focus on characterizing spatial region data when distinct classes of structural patterns are present. We propose a novel statistical approach based on a supervised framework for...
Despina Kontos, Vasileios Megalooikonomou, Marc J....
CVPR
2000
IEEE
15 years 11 months ago
Structure from Motion without Correspondence
A method is presented to recover 3D scene structure and camera motion from multiple images without the need for correspondence information. The problem is framed as finding the ma...
Frank Dellaert, Steven M. Seitz, Charles E. Thorpe...
ICRA
2007
IEEE
139views Robotics» more  ICRA 2007»
15 years 3 months ago
Evaluating the Roomba: A low-cost, ubiquitous platform for robotics research and education
— This paper presents the iRobot corporation’s Roomba vacuum as a low-cost resource for robotics research and education. Sensor and actuation models for unmodified Roombas are...
Ben Tribelhorn, Zachary Dodds
ROBOCUP
2005
Springer
164views Robotics» more  ROBOCUP 2005»
15 years 3 months ago
Laser-Based Localization with Sparse Landmarks
Abstract. Self-localization in dynamic environments is a central problem in mobile robotics and is well studied in the literature. One of the most popular methods is the Monte Carl...
Andreas Strack, Alexander Ferrein, Gerhard Lakemey...