Adaptive intelligent particle filter for visual localization of mobile robot

Lin Li; Guoli Wang; Rihui Chen; Hongxi Ou; Chaoran Chen; Xuemei Guo

doi:10.1117/12.2686497

10 August 2023 Adaptive intelligent particle filter for visual localization of mobile robot

Lin Li, Guoli Wang, Rihui Chen, Hongxi Ou, Chaoran Chen, Xuemei Guo

Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 127592F (2023) https://doi.org/10.1117/12.2686497
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China

Abstract

In the field of unmanned ships, which has attracted widly attention in recent years, the small terminals distributed on the huge ship need to independently judge the collision risks. However, the performance of terminal devices is significantly limited to ambient noise which affect the docking guidance. In this paper, Adaptive Intelligent Particle Filter (AIPF), an assisted tracking method based on Intelligent Particle Filter (IPF) is proposed to smooth and continuous state observation, thus effectively eliminating abnormal and leaping data. Specifically, this method regards the coordinates of the target in a period of time as a process with stationary independent increment, and the possible motion model is explored from an engineering perspective, so as to optimize low weight particles and adjust the number of particles dynamically. The experimental results on the ARMv7 platform demonstrate that even under the harsh conditions of computational power, the suggested method can still reduce particle degeneracy, improve the performance of particle swarm to better achieve auxiliary tracking.

Citation Download Citation

Lin Li, Guoli Wang, Rihui Chen, Hongxi Ou, Chaoran Chen, and Xuemei Guo "Adaptive intelligent particle filter for visual localization of mobile robot", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 127592F (10 August 2023); https://doi.org/10.1117/12.2686497

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KEYWORDS

Particles

Motion models

Particle filters

Gold

Mobile robots

Motion estimation

Particle swarm optimization

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