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» Bayesian Landmark Learning for Mobile Robot Localization
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ASC
2004
13 years 7 months ago
Recognizing environments from action sequences using self-organizing maps
In this paper, we describe development of a mobile robot which does unsupervised learning for recognizing an environment from action sequences. We call this novel recognition appr...
Seiji Yamada
ICRA
2000
IEEE
139views Robotics» more  ICRA 2000»
13 years 12 months ago
Appearance-Based Place Recognition for Topological Localization
This paper presents a new appearance-based place recognition system for topological localization. The method uses a panoramic vision system to sense the environment. Color images ...
Iwan Ulrich, Illah R. Nourbakhsh
ICRA
2005
IEEE
122views Robotics» more  ICRA 2005»
14 years 1 months ago
Supervised Learning of Places from Range Data using AdaBoost
— This paper addresses the problem of classifying places in the environment of a mobile robot into semantic categories. We believe that semantic information about the type of pla...
Óscar Martínez Mozos, Cyrill Stachni...
ICRA
2008
IEEE
149views Robotics» more  ICRA 2008»
14 years 2 months ago
Monocular range sensing: A non-parametric learning approach
Abstract— Mobile robots rely on the ability to sense the geometry of their local environment in order to avoid obstacles or to explore the surroundings. For this task, dedicated ...
Christian Plagemann, Felix Endres, Juergen Michael...
IROS
2006
IEEE
128views Robotics» more  IROS 2006»
14 years 1 months ago
Improving Data Association in Vision-based SLAM
— This paper presents an approach to vision-based simultaneous localization and mapping (SLAM). Our approach uses the scale invariant feature transform (SIFT) as features and app...
Arturo Gil, Óscar Reinoso, Óscar Mar...