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IBPRIA
2005
Springer
13 years 10 months ago
Monte Carlo Localization Using SIFT Features
The ability of finding its situation in a given environment is crucial for an autonomous agent. While navigating through a space, a mobile robot must be capable of finding its lo...
Arturo Gil, Óscar Reinoso, Maria Asunci&oac...
ICRA
2005
IEEE
167views Robotics» more  ICRA 2005»
13 years 10 months ago
Localization for Mobile Robots using Panoramic Vision, Local Features and Particle Filter
— In this paper we present a vision-based approach to self-localization that uses a novel scheme to integrate featurebased matching of panoramic images with Monte Carlo localizat...
Henrik Andreasson, André Treptow, Tom Ducke...
EUROS
2006
224views Robotics» more  EUROS 2006»
13 years 8 months ago
Metric Localization with Scale-Invariant Visual Features Using a Single Perspective Camera
Abstract. The Scale Invariant Feature Transform (SIFT) has become a popular feature extractor for vision-based applications. It has been successfully applied to metric localization...
Maren Bennewitz, Cyrill Stachniss, Wolfram Burgard...
ICPR
2008
IEEE
14 years 5 months ago
Local shape features for object recognition
We present a shape matching algorithm based on the chamfer distance transform which can be easily integrated into the well-known SIFT framework. The shape matching was designed to...
Bernd Heisele, Carlos Rocha
ICRA
2005
IEEE
149views Robotics» more  ICRA 2005»
13 years 10 months ago
A Markov Chain Monte Carlo Approach to Closing the Loop in SLAM
— The problem of simultaneous localization and mapping has received much attention over the last years. Especially large scale environments, where the robot trajectory loops back...
Michael Kaess, Frank Dellaert