为解决快速同步定位与地图构建算法因粒子退化导致SLAM(simultaneous location and mapping)估计精度不佳的问题,提出一种融合渐消自适应无迹粒子滤波与高斯分布重采样的FastSLAM算法。通过融合渐消滤波和无迹粒子滤波,产生一种自适应...为解决快速同步定位与地图构建算法因粒子退化导致SLAM(simultaneous location and mapping)估计精度不佳的问题,提出一种融合渐消自适应无迹粒子滤波与高斯分布重采样的FastSLAM算法。通过融合渐消滤波和无迹粒子滤波,产生一种自适应提议分布,利用高斯分布对高权重粒子进行分散得到新粒子。建立机器人运动模型和观测模型,并在仿真环境中进行性能验证。仿真结果表明:该算法能有效地缓解粒子退化,增加系统稳定性,提高SLAM估计精度。展开更多
To adjust the samples of filtering adaptively,an improved Gaussian particle filter algorithm based on Kullback-Leibler divergence(KLD)-sampling(KLGPF)is proposed in this paper.During the process of sampling,the algori...To adjust the samples of filtering adaptively,an improved Gaussian particle filter algorithm based on Kullback-Leibler divergence(KLD)-sampling(KLGPF)is proposed in this paper.During the process of sampling,the algorithm calculates the KLD to adjust the size of the particle set between the discrete probability density function of particles and the true posterior probability density function.KLGPF has significant effect when the noise obeys Gaussian distribution and the statistical characteristics of noise change abruptly.Simulation results show that KLGPF could maintain a good estimation effect when the noise statistics changes abruptly.Compared with the particle filter algorithm using KLD-sampling(KLPF),the speed of KLGPF increases by 28%under the same conditions.展开更多
文摘为解决快速同步定位与地图构建算法因粒子退化导致SLAM(simultaneous location and mapping)估计精度不佳的问题,提出一种融合渐消自适应无迹粒子滤波与高斯分布重采样的FastSLAM算法。通过融合渐消滤波和无迹粒子滤波,产生一种自适应提议分布,利用高斯分布对高权重粒子进行分散得到新粒子。建立机器人运动模型和观测模型,并在仿真环境中进行性能验证。仿真结果表明:该算法能有效地缓解粒子退化,增加系统稳定性,提高SLAM估计精度。
基金the China Postdoctoral Science Foundation(No.171980)the National Natural Science Foundation of China(Nos.61973160,51505221)Key Laboratory Fund of Science and Technology on Communication Networks(No.6142104180114).
文摘To adjust the samples of filtering adaptively,an improved Gaussian particle filter algorithm based on Kullback-Leibler divergence(KLD)-sampling(KLGPF)is proposed in this paper.During the process of sampling,the algorithm calculates the KLD to adjust the size of the particle set between the discrete probability density function of particles and the true posterior probability density function.KLGPF has significant effect when the noise obeys Gaussian distribution and the statistical characteristics of noise change abruptly.Simulation results show that KLGPF could maintain a good estimation effect when the noise statistics changes abruptly.Compared with the particle filter algorithm using KLD-sampling(KLPF),the speed of KLGPF increases by 28%under the same conditions.
