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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 18 / Book One

Robust Global Localization Using Clustered Particle Filtering

February 1, 2023

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Authors

Adam Milstein

Javier Nicolás Sánchez

and Evan Tang Williamson

Stanford University

DOI:


Abstract:

Global mobile robot localization is the problem of determining a robot’s pose in an environment, using sensor data, when the starting position is unknown. A family of probabilistic algorithms known as Monte Carlo Localization (MCL) is currently among the most popular methods for solving this problem. MCL algorithms represent a robot’s belief by a set of weighted samples, which approximate the posterior probability of where the robot is located by using a Bayesian formulation of the localization problem. This article presents an extension to the MCL algorithm, which addresses its problems when localizing in highly symmetrical environments; a situation where MCL is often unable to correctly track equally probable poses for the robot. The problem arises from the fact that sample sets in MCL often become impoverished, when samples are generated according to their posterior likelihood. Our approach incorporates the idea of clusters of samples and modifies the proposal distribution considering the probability mass of those clusters. Experimental results are presented that show that this new extension to the MCL algorithm successfully localizes in symmetric environments where ordinary MCL often fails.

Topics: AAAI

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HOW TO CITE:

Adam Milstein|| Javier Nicolás Sánchez|| and Evan Tang Williamson|| Stanford University Robust Global Localization Using Clustered Particle Filtering Proceedings of the AAAI Conference on Artificial Intelligence, 18 (2002) 581.

Adam Milstein|| Javier Nicolás Sánchez|| and Evan Tang Williamson|| Stanford University Robust Global Localization Using Clustered Particle Filtering AAAI 2002, 581.

Adam Milstein|| Javier Nicolás Sánchez|| and Evan Tang Williamson|| Stanford University (2002). Robust Global Localization Using Clustered Particle Filtering. Proceedings of the AAAI Conference on Artificial Intelligence, 18, 581.

Adam Milstein|| Javier Nicolás Sánchez|| and Evan Tang Williamson|| Stanford University. Robust Global Localization Using Clustered Particle Filtering. Proceedings of the AAAI Conference on Artificial Intelligence, 18 2002 p.581.

Adam Milstein|| Javier Nicolás Sánchez|| and Evan Tang Williamson|| Stanford University. 2002. Robust Global Localization Using Clustered Particle Filtering. "Proceedings of the AAAI Conference on Artificial Intelligence, 18". 581.

Adam Milstein|| Javier Nicolás Sánchez|| and Evan Tang Williamson|| Stanford University. (2002) "Robust Global Localization Using Clustered Particle Filtering", Proceedings of the AAAI Conference on Artificial Intelligence, 18, p.581

Adam Milstein|| Javier Nicolás Sánchez|| and Evan Tang Williamson|| Stanford University, "Robust Global Localization Using Clustered Particle Filtering", AAAI, p.581, 2002.

Adam Milstein|| Javier Nicolás Sánchez|| and Evan Tang Williamson|| Stanford University. "Robust Global Localization Using Clustered Particle Filtering". Proceedings of the AAAI Conference on Artificial Intelligence, 18, 2002, p.581.

Adam Milstein|| Javier Nicolás Sánchez|| and Evan Tang Williamson|| Stanford University. "Robust Global Localization Using Clustered Particle Filtering". Proceedings of the AAAI Conference on Artificial Intelligence, 18, (2002): 581.

Adam Milstein|| Javier Nicolás Sánchez|| and Evan Tang Williamson|| Stanford University. Robust Global Localization Using Clustered Particle Filtering. AAAI[Internet]. 2002[cited 2023]; 581.


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