scholarly journals ACK-MSCKF: Tightly-Coupled Ackermann Multi-State Constraint Kalman Filter for Autonomous Vehicle Localization

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4816 ◽  
Author(s):  
Fangwu Ma ◽  
Jinzhu Shi ◽  
Yu Yang ◽  
Jinhang Li ◽  
Kai Dai
Keyword(s):  
Kalman Filter ◽  
Pose Estimation ◽  
Process Model ◽  
State Constraint ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Tightly Coupled ◽  
Real World Datasets ◽  
Error State ◽  
Visual Measurements

Visual-Inertial Odometry (VIO) is subjected to additional unobservable directions under the special motions of ground vehicles, resulting in larger pose estimation errors. To address this problem, a tightly-coupled Ackermann visual-inertial odometry (ACK-MSCKF) is proposed to fuse Ackermann error state measurements and the Stereo Multi-State Constraint Kalman Filter (S-MSCKF) with a tightly-coupled filter-based mechanism. In contrast with S-MSCKF, in which the inertial measurement unit (IMU) propagates the vehicle motion and then the propagation is corrected by stereo visual measurements, we successively update the propagation with Ackermann error state measurements and visual measurements after the process model and state augmentation. This way, additional constraints from the Ackermann measurements are exploited to improve the pose estimation accuracy. Both qualitative and quantitative experimental results evaluated under real-world datasets from an Ackermann steering vehicle lead to the following demonstration: ACK-MSCKF can significantly improve the pose estimation accuracy of S-MSCKF under the special motions of autonomous vehicles, and keep accurate and robust pose estimation available under different vehicle driving cycles and environmental conditions. This paper accompanies the source code for the robotics community.

scholarly journals Novel MARG-Sensor Orientation Estimation Algorithm Using Fast Kalman Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Siwen Guo ◽  
Jin Wu ◽  
Zuocai Wang ◽  
Jide Qian
Keyword(s):  
Kalman Filter ◽  
Process Model ◽  
Attitude Determination ◽  
Angular Rate ◽  
Estimation Algorithm ◽  
Measurement Model ◽  
Estimation Accuracy ◽  
Orientation Estimation ◽  
Kinematic Equation ◽  
Sensor Orientation

Orientation estimation from magnetic, angular rate, and gravity (MARG) sensor array is a key problem in mechatronic-related applications. This paper proposes a new method in which a quaternion-based Kalman filter scheme is designed. The quaternion kinematic equation is employed as the process model. With our previous contributions, we establish the measurement model of attitude quaternion from accelerometer and magnetometer, which is later proved to be the fastest (computationally) one among representative attitude determination algorithms of such sensor combination. Variance analysis is later given enabling the optimal updating of the proposed filter. The algorithm is implemented on real-world hardware where experiments are carried out to reveal the advantages of the proposed method with respect to conventional ones. The proposed approach is also validated on an unmanned aerial vehicle during a real flight. Results show that the proposed one is faster than any other Kalman-based ones and even faster than some complementary ones while the attitude estimation accuracy is maintained.

scholarly journals End-Effector Pose Estimation in Complex Environments Using Complementary Enhancement and Adaptive Fusion of Multisensor

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Mingrui Luo ◽  
En Li ◽  
Rui Guo ◽  
Jiaxin Liu ◽  
Zize Liang
Keyword(s):  
Kalman Filter ◽  
Pose Estimation ◽  
Unscented Kalman Filter ◽  
Rbf Neural Network ◽  
Estimation Error ◽  
Estimation Method ◽  
Original Data ◽  
Measurement Unit ◽  
Complex Environments ◽  
End Effector

Redundant manipulators are suitable for working in narrow and complex environments due to their flexibility. However, a large number of joints and long slender links make it hard to obtain the accurate end-effector pose of the redundant manipulator directly through the encoders. In this paper, a pose estimation method is proposed with the fusion of vision sensors, inertial sensors, and encoders. Firstly, according to the complementary characteristics of each measurement unit in the sensors, the original data is corrected and enhanced. Furthermore, an improved Kalman filter (KF) algorithm is adopted for data fusion by establishing the nonlinear motion prediction of the end-effector and the synchronization update model of the multirate sensors. Finally, the radial basis function (RBF) neural network is used to adaptively adjust the fusion parameters. It is verified in experiments that the proposed method achieves better performances on estimation error and update frequency than the original extended Kalman filter (EKF) and unscented Kalman filter (UKF) algorithm, especially in complex environments.

scholarly journals Comparative Analysis between Error-State and Full-State Error Estimation for KF-Based IMU/GNSS Integration against IMU Faults

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4912 ◽  
Author(s):  
Wei Liu ◽  
Dan Song ◽  
Zhipeng Wang ◽  
Kun Fang
Keyword(s):  
Satellite System ◽  
Position Estimation ◽  
Measurement Unit ◽  
Multisensor Systems ◽  
System Integrity ◽  
Full State ◽  
Tightly Coupled ◽  
Integrity Assurance ◽  
Error State ◽  
State Error

Considering the inertial measurement unit (IMU) faults risk of an unmanned aerial vehicle (UAV), this paper provides an analysis of the error overboundings of position estimation in a tightly coupled IMU/global navigation satellite system (GNSS) integrated architecture under the IMU fault conditions using an error-state EKF-based approach and provides a comparison to a recently published EKF-based full state method. Simulation results show that both the error overboundings of the error-state and full-state EKFs can fit the state error against the IMU faults, but the error-state EKF is more suitable for UAV navigation system integrity assurance due to its higher calculation effciency. This study will be extended to the integrity monitoring of multisensor systems.

scholarly journals An Adaptive Pose Fusion Method for Indoor Map Construction

2021 ◽  
Vol 10 (12) ◽  
pp. 800
Author(s):  
Jinming Zhang ◽  
Lianrui Xu ◽  
Cuizhu Bao
Keyword(s):  
Pose Estimation ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Fusion Method ◽  
Static State ◽  
Matching Process ◽  
Map Construction ◽  
Mapping System ◽  
Weak Light ◽  
High Dynamics

The vision-based robot pose estimation and mapping system has the disadvantage of low pose estimation accuracy and poor local detail mapping effects, while the modeling environment has poor features, high dynamics, weak light, and multiple shadows, among others. To address these issues, we propose an adaptive pose fusion (APF) method to fuse the robot’s pose and use the optimized pose to construct an indoor map. Firstly, the proposed method calculates the robot’s pose by the camera and inertial measurement unit (IMU), respectively. Then, the pose fusion method is adaptively selected according to the motion state of the robot. When the robot is in a static state, the proposed method directly uses the extended Kalman filter (EKF) method to fuse camera and IMU data. When the robot is in a motive state, the weighted coefficient is determined according to the matching success rate of the feature points, and the weighted pose fusion (WPF) method is used to fuse camera and IMU data. According to the different states, a series of new poses of the robot are obtained. Secondly, the fusion optimized pose is used to correct the distance and azimuth angle of the laser points obtained by LiDAR, and a Gauss–Newton iterative matching process is used to match the corresponding laser points to construct an indoor map. Finally, a pose fusion experiment is designed, and the EuRoc data and the measured data are used to verify the effectiveness of this method. The experimental results confirm that this method provides higher pose estimation accuracy compared with the robust visual inertial odometry (ROVIO) and visual-inertial ORB-SLAM (VI ORB-SLAM) algorithms. Compared with the Cartographer algorithm, this method provides higher two-dimensional map modeling accuracy and modeling performance.

scholarly journals Dynamic State Estimation for Synchronous Machines Based on Adaptive Ensemble Square Root Kalman Filter

2019 ◽  
Vol 9 (23) ◽  
pp. 5200 ◽  
Author(s):  
Dongliang Nan ◽  
Weiqing Wang ◽  
Kaike Wang ◽  
Rabea Jamil Mahfoud ◽  
Hassan Haes Alhelou ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Random Noise ◽  
Measurement Noise ◽  
Dynamic State ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Square Root ◽  
Dynamic State Estimation ◽  
And Control

Dynamic state estimation (DSE) for generators plays an important role in power system monitoring and control. Phasor measurement unit (PMU) has been widely utilized in DSE since it can acquire real-time synchronous data with high sampling frequency. However, random noise is unavoidable in PMU data, which cannot be directly used as the reference data for power grid dispatching and control. Therefore, the data measured by PMU need to be processed. In this paper, an adaptive ensemble square root Kalman filter (AEnSRF) is proposed, in which the ensemble square root filter (EnSRF) and Sage–Husa algorithm are utilized to estimate measurement noise online. Simulation results obtained by applying the proposed method show that the estimation accuracy of AEnSRF is better than that of ensemble Kalman filter (EnKF), and AEnSRF can track the measurement noise when the measurement noise changes.

scholarly journals Integration of GNSS-IMU for increasing the observation accuracy in Condensed Areas (Infrastructure and Forest Canopies)

2019 ◽  
Vol 94 ◽  
pp. 03015
Author(s):  
Mokhamad Nur Cahyadi ◽  
Irene Rwabudandi
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Kalman Filters ◽  
Measurement Unit ◽  
Gps Receiver ◽  
Satellite Navigation System ◽  
Loosely Coupled ◽  
Advantages And Disadvantages ◽  
Gps Satellites ◽  
Tightly Coupled

Position determination using satellite navigation system has grown significantly. It provides geospatial with global coverage called GNSS (Global Navigation System Satellite). GNSS satellites consists of GLONASS, GPS (Global positioning system) and Galileo.GPS is the most commonly used system and it is known to its capability to determine 3D position on the surface of the earth. In order to determine the position, a GPS receiver must be able to receive signals from at least four GPS satellites. However, the determination of position in condensed areas such as tunnels, area surrounded by high rise buildings, highly forested and in other closely-knit areas is not achieved because satellite signals cannot reach the receiver in the above-mentioned areas and also others where the signals are reflected before being received by a GPS receiver. In this paper, we present the algorithm to fuse GPS and the inertial measurement unit (IMU) to enable positioning in the above-mentioned Condensed Areas. The standard deviations of the two measurements show that GPS-IMU is better than GPS alone, the standard deviation when satellite outages occurred is - 4.57475 for GPS-IMU measurements and 0.218675 for GPS observations. We presented the results in graphics and it shows that GPS measurements are easily disturbed by external influence such as multipath but GPS-IMU graphic is continuous and robust. The advantages and disadvantages of GPS and INS are complementary and make them work together to enable the accurate measurements in the areas mentioned above. Integration of INS and GNSS can be classified into three types, loosely coupled Kalman filter, tightly coupled Kalman filters and ultratight coupled Kalman filter. In this research we used loosely coupled Kalman filter and tightly coupled Kalman filters to combine GPS and INS in one system.

INS/GPS Tightly-coupled Integration using Adaptive Unscented Particle Filter

2010 ◽  
Vol 63 (3) ◽  
pp. 491-511 ◽  
Author(s):  
Junchuan Zhou ◽  
Stefan Knedlik ◽  
Otmar Loffeld
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Unscented Kalman Filter ◽  
Estimation Accuracy ◽  
Proposal Distribution ◽  
Sampling Step ◽  
Computational Burden ◽  
Monte Carlo Experiments ◽  
Unscented Particle Filter ◽  
Tightly Coupled

With the rapid developments in computer technology, the particle filter (PF) is becoming more attractive in navigation applications. However, its large computational burden still limits its widespread use. One approach for reducing the computational burden without degrading the system estimation accuracy is to combine the PF with other filters, i.e., the extended Kalman filter (EKF) or the unscented Kalman filter (UKF). In this paper, the a posteriori estimates from an adaptive unscented Kalman filter (AUKF) are used to specify the PF importance density function for generating particles. Unlike the sequential importance sampling re-sampling (SISR) PF, the re-sampling step is not required in the algorithm, because the filter does not reuse the particles. Hence, the filter computational complexity can be reduced. Besides, the latest measurements are used to improve the proposal distribution for generating particles more intelligently. Simulations are conducted on the basis of a field-collected 3D UAV trajectory. GPS and IMU data are simulated under the assumption that a NovAtel DL-4plus GPS receiver and a Landmark™ 20 MEMS-based IMU are used. Navigation under benign and highly reflective signal environments are considered. Monte Carlo experiments are made. Numerical results show that the AUPF with 100 particles can present improved system estimation accuracy with an affordable computational burden when compared with the AEKF and AUKF algorithms.

scholarly journals UWB/PDR Tightly Coupled Navigation with Robust Extended Kalman Filter for NLOS Environments

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Xin Li ◽  
Yan Wang ◽  
Kourosh Khoshelham
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Dead Reckoning ◽  
Step Length ◽  
Noise Signal ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Fusion Algorithm ◽  
Tightly Coupled

The fusion of ultra-wideband (UWB) and inertial measurement unit (IMU) is an effective solution to overcome the challenges of UWB in nonline-of-sight (NLOS) conditions and error accumulation of inertial positioning in indoor environments. However, existing systems are based on foot-mounted or body-worn IMUs, which limit the application of the system to specific practical scenarios. In this paper, we propose the fusion of UWB and pedestrian dead reckoning (PDR) using smartphone IMU, which has the potential to provide a universal solution to indoor positioning. The PDR algorithm is based on low-pass filtering of acceleration data and time thresholding to estimate the step length. According to different movement patterns of pedestrians, such as walking and running, several step models are comparatively analyzed to determine the appropriate model and related parameters of the step length. For the PDR direction calculation, the Madgwick algorithm is adopted to improve the calculation accuracy of the heading algorithm. The proposed UWB/PDR fusion algorithm is based on the extended Kalman filter (EKF), in which the Mahalanobis distance from the observation to the prior distribution is used to suppress the influence of abnormal UWB data on the positioning results. Experimental results show that the algorithm is robust to the intermittent noise, continuous noise, signal interruption, and other abnormalities of the UWB data.

scholarly journals A Robust Adaptive Cubature Kalman Filter Based on SVD for Dual-Antenna GNSS/MIMU Tightly Coupled Integration

2021 ◽  
Vol 13 (10) ◽  
pp. 1943
Author(s):  
Cheng Pan ◽  
Nijia Qian ◽  
Zengke Li ◽  
Jingxiang Gao ◽  
Zhenbin Liu ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Satellite System ◽  
Integrated System ◽  
Measurement Unit ◽  
Attitude Angle ◽  
Cubature Kalman Filter ◽  
Tightly Coupled ◽  
Heading Angle ◽  
Robust Adaptive

In complex urban environments, a single Global Navigation Satellite System (GNSS) is often not ideal for navigation due to a lack of sufficient visible satellites. Additionally, the heading angle error of a GNSS/micro-electro-mechanical system–grade inertial measurement unit (MIMU) tightly coupled integration based on the single antenna is large, and the attitude angle, velocity, and position calculated therein all have large errors. Considering the above problems, this paper designs a tightly coupled integration of GNSS/MIMU based on two GNSS antennas and proposes a singular value decomposition (SVD)-based robust adaptive cubature Kalman filter (SVD-RACKF) according to the model characteristics of the integration. In this integration, the high-accuracy heading angle of the carrier is obtained through two antennas, and the existing attitude angle information is used as the observation to constrain the filtering estimation. The proposed SVD-RACKF uses SVD to stabilize the numerical accuracy of the recursive filtering. Furthermore, the three-stage equivalent weight function and the adaptive adjustment factor are constructed to suppress the influence of the gross error and the abnormal state on the parameter estimation, respectively. A set of real measured data was employed for testing and analysis. The results show that dual antennas and dual systems can improve the positioning performance of the integrated system to a certain extent, and the proposed SVD-RACKF can accurately detect the gross errors of the observations and effectively suppress them. Compared with the cubature Kalman filter, the proposed filtering algorithm is more robust, with higher accuracy and reliability of parameter estimation.

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