scholarly journals Global Vision Localization of Indoor Service Robot Based on Improved Iterative Extended Kalman Particle Filter Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bingshan Hu ◽  
Qingxiao Yu ◽  
Hongliu Yu
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Real Time ◽  
Posterior Probability ◽  
Mobile Robot Navigation ◽  
Estimation Accuracy ◽  
Service Robot ◽  
Mobile Service ◽  
Posterior Probability Distribution ◽  
Particle Filter Algorithm

Localization is the primary problem of mobile robot navigation. Monte Carlo localization based on particle filter has better accuracy and is easier to implement, but there is also the problem of particle degradation. In this paper, the iterative extended Kalman filter is optimized by the Levenberg-Marquardt optimization method. An improved particle filter algorithm based on the upon optimized iterative Kalman filter is proposed, and the importance probability density function of the particle filter is generated by the maximum posterior probability estimation of the improved iterative Kalman filter. Simulation results of the improved particle filter algorithm show that the algorithm can approximate the state posterior probability distribution more closely with fewer sampled particles under the premise of ensuring sufficient state estimation accuracy. Meanwhile, the computation is reduced and the real-time performance is enhanced. Finally, the algorithm is validated on the indoor mobile service robot. The experimental results show that the localization algorithm’s accuracy meets requirement for real-time localizing of the restaurant service robot.

Research of Video Tracking Algorithm Based on the Blob Analysis

2011 ◽  
Vol 383-390 ◽  
pp. 1185-1189
Author(s):  
Zeng Ping Zhang ◽  
Shu Hua Li
Keyword(s):  
Kalman Filter ◽  
Probability Distribution ◽  
Particle Filter ◽  
Posterior Probability ◽  
Video Tracking ◽  
Tracking Algorithm ◽  
Posterior Probability Distribution ◽  
Blob Analysis ◽  
Further Development ◽  
Target Locating

To the video that contains the target, a method is proposed to create the background model based on the mixed Gauss. And the target locating method based on the blob analysis and blob filtering, the anti-noise ability and filter robustness of tracking is improved. The kalman filter and the particle filter are separately used to pass and update the foreground target’s posterior probability distribution. Finally the kalman filter and the particle filter's are compared and that builds the foundation of the further development.

scholarly journals Real Time Color Object Tracking on Cell Broadband Engine Using Particle Filters

2010 ◽  
Vol 14 (3) ◽  
pp. 272-280 ◽  
Author(s):  
Norikazu Ikoma ◽  
◽  
Akihiro Asahara ◽  
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Real Time ◽  
Visual Tracking ◽  
Single Object ◽  
Cell Broadband Engine ◽  
Time Performance ◽  
Large Size ◽  
Particle Filter Algorithm ◽  
Local Store

Real time visual tracking by particle filter has been implemented on Cell Broadband Engine in parallel. Major problem for the implementation is small size of Local Store (LS) in SPEs (Synergistic PEs), which are computational cores, to deal with image of large size. As a first step for the implementation, we focus on color single object tracking, which is one of the most simple case of visual tracking. By elaborating to compress the color extracted image into bit-wise representation of binary image, all information of the color extracted image can be stored in LS for 640×480 size of original image. By applying our previous implementation of general particle filter algorithm on Cell/B.E. to this specific case, we have achieved real time performance of visual tracking on PlayStation®3 about 7 fps with a camera of maximum 15 fps.

scholarly journals Implementation of Hybrid Model of Particle Filter and Kalman Filter based Real-Time Tracking for handling Occlusion on Beagleboard-xM

2014 ◽  
Vol 95 (7) ◽  
pp. 31-37 ◽  
Author(s):  
Jharna Majumdar ◽  
Parashar Dhakal ◽  
Nabin Sharma Rijal ◽  
Amar Mani Aryal ◽  
Nilesh Kumar Mishra
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Real Time ◽  
Hybrid Model ◽  
Real Time Tracking

scholarly journals A Strong Tracking Mixed-Degree Cubature Kalman Filter Method and Its Application in a Quadruped Robot

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2251 ◽  
Author(s):  
Jikai Liu ◽  
Pengfei Wang ◽  
Fusheng Zha ◽  
Wei Guo ◽  
Zhenyu Jiang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Real Time ◽  
Quadruped Robot ◽  
The State ◽  
Estimation Accuracy ◽  
Robot System ◽  
Calculation Time ◽  
Motion State ◽  
Cubature Kalman Filter

The motion state of a quadruped robot in operation changes constantly. Due to the drift caused by the accumulative error, the function of the inertial measurement unit (IMU) will be limited. Even though multi-sensor fusion technology is adopted, the quadruped robot will lose its ability to respond to state changes after a while because the gain tends to be constant. To solve this problem, this paper proposes a strong tracking mixed-degree cubature Kalman filter (STMCKF) method. According to system characteristics of the quadruped robot, this method makes fusion estimation of forward kinematics and IMU track. The combination mode of traditional strong tracking cubature Kalman filter (TSTCKF) and strong tracking is improved through demonstration. A new method for calculating fading factor matrix is proposed, which reduces sampling times from three to one, saving significantly calculation time. At the same time, the state estimation accuracy is improved from the third-degree accuracy of Taylor series expansion to fifth-degree accuracy. The proposed algorithm can automatically switch the working mode according to real-time supervision of the motion state and greatly improve the state estimation performance of quadruped robot system, exhibiting strong robustness and excellent real-time performance. Finally, a comparative study of STMCKF and the extended Kalman filter (EKF) that is commonly used in quadruped robot system is carried out. Results show that the method of STMCKF has high estimation accuracy and reliable ability to cope with sudden changes, without significantly increasing the calculation time, indicating the correctness of the algorithm and its great application value in quadruped robot system.

scholarly journals Improved Interacting Multiple Model Particle Filter Algorithm

2018 ◽  
Vol 36 (1) ◽  
pp. 169-175 ◽  
Author(s):  
Qiaoran Liu ◽  
Xun Yang
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Multiple Model ◽  
Tracking Accuracy ◽  
Proposal Distribution ◽  
Model Particle ◽  
Interactive Multiple Model ◽  
Particle Filter Algorithm

For the issue of limited filtering accuracy of interactive multiple model particle filter algorithm caused by the resampling particles don't contain the latest observation information, we made improvements on interactive multiple model particle filter algorithm in this paper based on mixed kalman particle filter algorithm. Interactive multiple model particle filter algorithm is proposed. In addition, the composed methods influence to tracking accuracy are discussed. In the new algorithm the system state estimation is generated with unscented kalman filter (UKF) first and then use the extended kalman filter (EKF) to get the proposal distribution of the particles, taking advantage of the measure information to update the particles' state. We compare and analyze the target tracking performance of the proposed algorithm of IMM-MKPF in this paper, IMM-UPF and IMM-EPF through the simulation experiment. The results show that the tracking accuracy of the proposed algorithm is superior to other two algorithms. Thus, the new method in this paper is effective. The method is of important to improve tracking accuracy further for maneuvering target tracking under the non-linear and non-Gaussian circumstances.

scholarly journals Multiple Road-Objects Detection and Tracking for Autonomous Driving

2021 ◽  
Author(s):  
Wael Farag ◽  
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
High Performance ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Autonomous Driving ◽  
Estimation Accuracy ◽  
Time Performance ◽  
Detection And Tracking ◽  
Objects Detection

In this paper, a real-time road-Object Detection and Tracking (LR_ODT) method for autonomous driving is proposed. The method is based on the fusion of lidar and radar measurement data, where they are installed on the ego car, and a customized Unscented Kalman Filter (UKF) is employed for their data fusion. The merits of both devices are combined using the proposed fusion approach to precisely provide both pose and velocity information for objects moving in roads around the ego car. Unlike other detection and tracking approaches, the balanced treatment of both pose estimation accuracy and its real-time performance is the main contribution in this work. The proposed technique is implemented using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. Simulation studies have been carried out to evaluate the performance of the LR_ODT for tracking bicycles, cars, and pedestrians. Moreover, the performance of the UKF fusion is compared to that of the Extended Kalman Filter fusion (EKF) showing its superiority. The UKF has outperformed the EKF on all test cases and all the state variable levels (-24% average RMSE). The employed fusion technique show how outstanding is the improvement in tracking performance compared to the use of a single device (-29% RMES with lidar and -38% RMSE with radar).

scholarly journals Pipeline Gaussian Particle Filter and Hardware Design for Attitude Estimation with UAV

2020 ◽  
Vol 2020 ◽  
pp. 1-6 ◽  
Author(s):  
Yali Xue ◽  
Hu Chen ◽  
Jie Chen ◽  
Jiahui Wang
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Gaussian Noise ◽  
Estimation Algorithm ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Gaussian State ◽  
Time Performance ◽  
Simulation Results ◽  
Non Gaussian

This paper based on the Gaussian particle filter (GPF) deals with the attitude estimation of UAV. GPF algorithm has better estimation accuracy than the general nonlinear non-Gaussian state estimation and is usually used to improve the system’s real-time performance whose noise is specific such as Gaussian noise during the mini UAV positioning and navigation. The attitude estimation algorithm is implemented on FPGA to verify the effectiveness of the Gaussian particle filter. Simulation results have illustrated that the GPF algorithm is effective and has better real-time performance than that of the particle filter.

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