基于CDKF滤波算法的智能车组合定位技术的探讨

Probe into Intelligent Vehicle Combined Positioning Technology Based on CDKF Filter Algorithm

提出了一种新的CDKF滤波算法,用此算法来替代扩展卡尔曼滤波器算法,由此迭代更新其观测到的数据,目的是提高智能车系统的定位精度,使提议分布更趋近于后验概率分布,并且能够准确估算智能车的位姿进而对其位置进行特征地图更新。此算法的优越性在于既能保证智能汽车定位精度,又能减少计算复杂度。由于车辆系统估算性能得到提高,使得粒子滤波算法稳定性获得增强。

A new CDKF filter algorithm was proposed,which is used to replace the extended Kalman filter algorithm,based on which the data observed by it is updated iteratively,with the aim to improve the positioning accuracy of the intelligent vehicle system and make the proposed distribution closer to posterior probability distribution,with the ability to accurately estimate the position and attitude ofan intelligent vehicle and then update the feature map ofits position.The superiority ofthis algorithm lies in its ability to ensure the positioning accuracy of intelligent vehicles while reducing computational complexity.Due to the improved estimation performance of the vehicle system,the stability of the particle filter algorithm has been enhanced.