本文選題：同時定位與地圖構建 + Rao-Blackwellized粒子濾波器　； 參考：《計算機工程》2016年11期

【摘要】：針對Rao-Blackwellized粒子濾波器(RBPF)重采樣過程存在粒子衰竭、提議分布精確度不高的問題,提出一種改進的RBPF算法。為提高RBPF算法提議分布精確性,在改進的算法中將機器人里程計信息和激光傳感器采集的距離信息進行融合,在算法中引入粒子群尋優(yōu)策略,通過粒子間能效吸引力來調整采樣粒子集,同時對重采樣中權值較小的粒子進行遺傳變異操作,緩解粒子枯竭現(xiàn)象,提高機器人位姿估計一致性,并維持粒子集的多樣性。在基于機器人操作系統(tǒng)和配有URG激光傳感器的Pioneer3-DX機器人平臺上對改進RBPF算法進行可靠性驗證。實驗結果表明,改進算法在兼顧粒子集多樣性的同時能顯著提高機器人位姿估計精確性。
[Abstract]:An improved RBPF algorithm is proposed to solve the problem of particle failure and low accuracy of proposed distribution in the resampling process of Rao-Blackwellized particle filter (RBPF). In order to improve the distribution accuracy of RBPF algorithm, the robot odometer information and the distance information collected by laser sensor are fused in the improved algorithm, and the particle swarm optimization strategy is introduced in the algorithm. The sampling particle set is adjusted by energy efficiency attraction among particles, and the particles with small weight in resampling are operated by genetic variation, which can alleviate the phenomenon of particle depletion, improve the consistency of robot pose estimation, and maintain the diversity of particle sets. The reliability of the improved RBPF algorithm is verified on the Pioneer3-DX robot platform based on robot operating system and with URG laser sensor. Experimental results show that the improved algorithm can significantly improve the accuracy of robot pose estimation while taking into account the diversity of particle sets.
【作者單位】： 重慶郵電大學信息無障礙工程研發(fā)中心;
【基金】：國家科技部國際合作項目(2010DFA12160) 重慶市科技攻關項目(CSTC,2010AA2055)
【分類號】：TP242;TP18
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本文編號：2020329