Indoor Mobile Robotics at Grima, PUC

@article{Caro2012IndoorMR,
  title={Indoor Mobile Robotics at Grima, PUC},
  author={Luis Alberto Caro and Javier Correa and Pablo Espinace and Daniel Langdon and Daniel Maturana and Rub{\'e}n Mitnik and Sebastian Montabone and Stefan Pszcz{\'o}lkowski and Anita Araneda and Domingo Mery and Miguel Torres and Alvaro Soto},
  journal={Journal of Intelligent \& Robotic Systems},
  year={2012},
  volume={66},
  pages={151-165}
}
  • L. Caro, J. Correa, A. Soto
  • Published 1 April 2012
  • Computer Science
  • Journal of Intelligent & Robotic Systems
This paper describes the main activities and achievements of our research group on Machine Intelligence and Robotics (Grima) at the Computer Science Department, Pontificia Universidad Catolica de Chile (PUC). Since 2002, we have been developing an active research in the area of indoor autonomous social robots. Our main focus has been the cognitive side of Robotics, where we have developed algorithms for autonomous navigation using wheeled robots, scene recognition using vision and 3D range… 
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References

SHOWING 1-10 OF 88 REFERENCES
Introduction to Autonomous Mobile Robots
TLDR
Bringing together all aspects of mobile robotics into one volume, Introduction to Autonomous Mobile Robots can serve as a textbook or a working tool for beginning practitioners.
Active Visual Perception for Mobile Robot Localization
TLDR
This paper presents an active perception strategy for a mobile robot provided with a visual sensor mounted on a pan-tilt mechanism and indicates that, in terms of accuracy of robot localization, the proposed approach decreases mean average error and standard deviation with respect to a passive perception scheme.
A statistical approach to simultaneous mapping and localization for mobile robots
TLDR
A novel sampling algorithm to solving the simultaneous mapping and localization (SLAM) problem in indoor environments from a Bayesian statistics perspective and derives three sampling algorithms based on importance sampling.
Human Detection in Indoor Environments Using Multiple Visual Cues and a Mobile Robot
TLDR
This paper shows an approach to robustly detect people in indoor environments using a mobile platform that uses a stereo vision system that yields a stereo pair from which a disparity image is obtained.
Simultaneous map building and localization for an autonomous mobile robot
  • J. Leonard, H. Durrant-Whyte
  • Computer Science
    Proceedings IROS '91:IEEE/RSJ International Workshop on Intelligent Robots and Systems '91
  • 1991
Discusses a significant open problem in mobile robotics: simultaneous map building and localization, which the authors define as long-term globally referenced position estimation without a priori
Monte Carlo localization for mobile robots
TLDR
The Monte Carlo localization method is introduced, where the probability density is represented by maintaining a set of samples that are randomly drawn from it, and it is shown that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location.
Mobile robot localization using the Hausdorff distance
TLDR
Good estimates of these variables were obtained during tests performed using a differential drive robot, thus demonstrating that the approach provides an accurate, reliable and computationally feasible alternative for indoor robot localization and autonomous navigation.
Real-Time Robot Localization in Indoor Environments Using Structural Information
TLDR
A novel approach for real-time mobile robot localization in structured indoor environments by implementing a Monte Carlo Localization strategy over a map of line segments rather than a grid-based map, thus allowing for speed improvements and robustness in position tracking.
Shakey the Robot
Abstract : From 1960 through 1972, the Artificial Intelligence Center at SRI conducted research on a mobile robot system nicknamed "Shakey." Endowed with a limited ability to perceive and model its
Statistical Inference in Mapping and Localization for Mobile Robots
TLDR
This paper exploits a particular factorization of this distribution that allows for the implementation of an Importance Sampling algorithm and shows the results obtained when applied to a data set obtained by a robot navigating inside an office building type of indoor environment.
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