基于仿生算法改进粒子滤波的SLAM算法精度预测

为解决传统粒子滤波算法重采样时产生的样本退化及样本贫乏带来的机器人定位与建图精度下降问题,提出一种基于改进仿生算法的粒子滤波.该算法将粒子最新时刻的观测与状态信息引入亮度公式,并将萤火虫的优胜劣汰和位置更新机制融入粒子滤波算法,以提高粒子的滤波能力.为保证算法的收敛速度和预测精度,在萤火虫位置更新过程中引入自适应调整步长进行即时修正;基于标准粒子滤波重采样的缺陷,采取分步重采样策略,通过偏差修正指数加权算法制定高效的舍小保大方案,并合理使用剩余大权值粒子完成粒子的复制和添加.仿真验证表明,所提出的改进算法可以明显提高传统粒子滤波的预测精度,且应用到基于移动机器人运动模型的定位与建图时可保持较高的定位精度和较好的稳定性.

Accuracy predition of SLAM algorithm based on bionic algorithm to improve particle filter

To solve the problem of poor accuracy of robot localization and mapping caused by sample degradation and sample depletion caused by the traditional particle filter algorithm while re-sampling, a particle filter method is proposed based on an improved firefly algorithm. The algorithm introduces the observation and state information of the latest moment of the particle into the brightness formula, and the firefly''s survival of the fittest and position update mechanism are integrated into the particle filter algorithm, which improves the filtering ability of the particle. To ensure the convergence speed and prediction accuracy of the algorithm, the adaptive adjustment step is introduced in the firefly position update process for immediate correction. Based on the defect of standard particle filter resampling, step-by-step resampling strategy is adopted, an efficient scheme for abandoning small and retain large values is established by the deviation correction exponentially weighted average algorithm. The remaining large weight particles are used to complete the copying and adding of the particle. The simulation results show that the proposed algorithm improves the prediction accuracy of the traditional particle filter, and it can maintain high positioning accuracy and stability when applied to the localization and mapping based on mobile robot motion models.