为提高移动机器人在拥挤、混杂的室内环境中的定位能力,提出了在图像显著特征区域内提取积分不变特征-LSRII(local salient region integral invariant)特征的方法,并将LSRII特征应用到粒子滤波定位中,实现机器人在室内环境下的全局定...为提高移动机器人在拥挤、混杂的室内环境中的定位能力,提出了在图像显著特征区域内提取积分不变特征-LSRII(local salient region integral invariant)特征的方法,并将LSRII特征应用到粒子滤波定位中,实现机器人在室内环境下的全局定位。实验结果表明,所提出的定位方法在拥挤、混杂的室内环境中能够实现可靠的定位。展开更多
To solve the problem of information fusion in the strapdown inertial navigation system(SINS)/celestial navigation system(CNS)/global positioning system(GPS) integrated navigation system described by the nonlinear/non-...To solve the problem of information fusion in the strapdown inertial navigation system(SINS)/celestial navigation system(CNS)/global positioning system(GPS) integrated navigation system described by the nonlinear/non-Gaussian error models,a new algorithm called the federated unscented particle filtering(FUPF) algorithm was introduced.In this algorithm,the unscented particle filter(UPF) served as the local filter,the federated filter was used to fuse outputs of all local filters,and the global filter result was obtained.Because the algorithm was not confined to the assumption of Gaussian noise,it was of great significance to integrated navigation systems described by the non-Gaussian noise.The proposed algorithm was tested in a vehicle's maneuvering trajectory,which included six flight phases:climbing,level flight,left turning,level flight,right turning and level flight.Simulation results are presented to demonstrate the improved performance of the FUPF over conventional federated unscented Kalman filter(FUKF).For instance,the mean of position-error decreases from(0.640×10-6 rad,0.667×10-6 rad,4.25 m) of FUKF to(0.403×10-6 rad,0.251×10-6 rad,1.36 m) of FUPF.In comparison of the FUKF,the FUPF performs more accurate in the SINS/CNS/GPS system described by the nonlinear/non-Gaussian error models.展开更多
文摘为提高移动机器人在拥挤、混杂的室内环境中的定位能力,提出了在图像显著特征区域内提取积分不变特征-LSRII(local salient region integral invariant)特征的方法,并将LSRII特征应用到粒子滤波定位中,实现机器人在室内环境下的全局定位。实验结果表明,所提出的定位方法在拥挤、混杂的室内环境中能够实现可靠的定位。
基金Project(60535010) supported by the National Nature Science Foundation of China
文摘To solve the problem of information fusion in the strapdown inertial navigation system(SINS)/celestial navigation system(CNS)/global positioning system(GPS) integrated navigation system described by the nonlinear/non-Gaussian error models,a new algorithm called the federated unscented particle filtering(FUPF) algorithm was introduced.In this algorithm,the unscented particle filter(UPF) served as the local filter,the federated filter was used to fuse outputs of all local filters,and the global filter result was obtained.Because the algorithm was not confined to the assumption of Gaussian noise,it was of great significance to integrated navigation systems described by the non-Gaussian noise.The proposed algorithm was tested in a vehicle's maneuvering trajectory,which included six flight phases:climbing,level flight,left turning,level flight,right turning and level flight.Simulation results are presented to demonstrate the improved performance of the FUPF over conventional federated unscented Kalman filter(FUKF).For instance,the mean of position-error decreases from(0.640×10-6 rad,0.667×10-6 rad,4.25 m) of FUKF to(0.403×10-6 rad,0.251×10-6 rad,1.36 m) of FUPF.In comparison of the FUKF,the FUPF performs more accurate in the SINS/CNS/GPS system described by the nonlinear/non-Gaussian error models.
