Monte Carlo localization

Known as: MCL 
Monte Carlo localization (MCL), also known as particle filter localization, is an algorithm for robots to localize using a particle filter. Given a… (More)
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Topic mentions per year

Topic mentions per year

1999-2017
010203019992017

Papers overview

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2010
2010
Mobile robots operating in real and populated environments usually execute tasks that require accurate knowledge on their… (More)
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Highly Cited
2006
Highly Cited
2006
Localization is crucial to many applications in wireless sensor networks. This article presents a range-free anchor-based… (More)
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Highly Cited
2005
Highly Cited
2005
For most outdoor applications, systems such as GPS provide users with accurate position estimates. However, reliable range-based… (More)
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Highly Cited
2005
Highly Cited
2005
In this paper, we present a vision-based approach to mobile robot localization that integrates an image-retrieval system with… (More)
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Highly Cited
2004
Highly Cited
2004
Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor… (More)
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Highly Cited
2003
Highly Cited
2003
This paper presents a fast approach for vision-based self-localization in RoboCup. The vision system extracts the features… (More)
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Highly Cited
2001
Highly Cited
2001
Mobile robot localization is the problem of determining a robot’s pose from sensor data. This article presents a family of… (More)
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Highly Cited
2000
Highly Cited
2000
Monte Carlo localization (MCL) is a Bayesian algorithm for mobile robot localization based on particle filters, which has enjoyed… (More)
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Highly Cited
1999
Highly Cited
1999
To navigate reliably in indoor environments, a mobile robot must know where it is. Thus, reliable position estimation is a key… (More)
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Highly Cited
1999
Highly Cited
1999
This paper presents a new algorithm for mobile robot localization, called Monte Carlo Localization (MCL). MCL is a version of… (More)
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