A Visual Based Extended Monte Carlo Localization for Autonomous Mobile Robots

Shang, Wen; Su, D.

doi:10.5772/4989

Cited by 2 publications

(1 citation statement)

References 7 publications

(7 reference statements)

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“…Although various efforts [6]- [10] have been made to improve the performance of conventional MCL in difference aspect, the standard MCL and its improving methods (MCLs) still suffer some disadvantages. One of the key problems is that MCLs have to sample a large amount of particles uniformly over the state space at the very initial steps, so as to place enough particles near the robot's real pose.…”

Section: Introductionmentioning

confidence: 99%

Active particle in MCL: An evolutionary view

Min

Chen

Luo

2009

2009 International Conference on Information and Automation

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Mobile robot localization is the task of determining a robot's pose in a known environment, which is one of the most important problems in mobile robotics. The state-of-the-art Monte Carlo Localization (MCL) algorithm requires a large amount of particles and thus converges slowly. Also, MCL performs poorly in low noise sensor input.This paper develops a more efficient algorithm called APMCL. It puts the process of MCL in the CLONAL framework, which is a computational implementation of the clonal selection principle in immune system, and utilizes Particle Swarm Optimization to improve the mutation process. The key idea of APMCL is to take the perceptual model ) | ( t t x y p as the fitness function and then carry out a heuristic searching step on the particles generated in MCL. It drives the particles towards the regions where the value of the desired posterior function is large.As a result, the particles are no longer passively waiting for being filtered in Sequential Monte Carlo process. In contrast, they will actively adapt themselves to much more valuable pose (with higher fitness value) by cognitive study (i.e. mutation) and swarm study (PSO).Experimental results show that APMCL reduces the number of particles needed in conventional MCL and improves the performance of the algorithm regarding to the computation time as well as the localization precision. Active Particle in MCL: An Evolutionary ViewHua-qing MIN, Huan CHEN, Rong-hua LUO M 978-1-4244-3608-8/09/$25.00

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Section: Introductionmentioning

confidence: 99%

Active particle in MCL: An evolutionary view

Min

Chen

Luo

2009

2009 International Conference on Information and Automation

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Vision-based Monte Carlo localization for RoboCup Humanoid Kid-Size League

Nagi

Adiprawita

Mutijarsa

2014

2014 13th International Conference on Control Automation Robotics &Amp; Vision (ICARCV)

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Localization is the most fundamental ability for winning the RoboCup Humanoid League Competition. In this paper, we present a vision-based localization method called Monte Carlo Localization (MCL) to deal with the limited landmarks left in RoboCup, such as the yellow goal posts and field markers. In the beginning, we give brief explanation of perception system. Next, we give detailed implementation of MCL, an improvement of the resampling step that has been develop before, and the process of estimating the localization result. We perform all experiments on our humanoid robot named Zared_v1.0. Results show that the modified resampling technique in MCL give better result in estimating robot position and orientation on normal and kidnapping condition.

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A Visual Based Extended Monte Carlo Localization for Autonomous Mobile Robots

Cited by 2 publications

References 7 publications

Active particle in MCL: An evolutionary view

Active particle in MCL: An evolutionary view

Vision-based Monte Carlo localization for RoboCup Humanoid Kid-Size League

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