scholarly journals An IMU/ODM/UWB Joint Localization System Based on Modified Cubature Kalman Filtering

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4823
Author(s):  
Chao Tang ◽  
Chengyang He ◽  
Lihua Dou
Keyword(s):  
State Estimation ◽  
Kalman Filtering ◽  
Adaptive Filtering ◽  
Inertial Measurement Unit ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Process Noise ◽  
Computational Burden ◽  
Localization System ◽  
Federated Kalman Filter

In this article, a multisensor joint localization system is proposed based on modified cubature Kalman filtering, which aims to improve the accuracy of state estimation under a moderate computational burden in the presence of high process noise. Specifically, first, the covariance of process noise is matched based on adaptive filtering. The inertial measurement unit (IMU), odometer (ODM), and ultra-wideband (UWB) information acquired by the associated sensors is then employed to augment the system state and are fused to lower the influence of process noise. In the presented localization setting, all sensors (IMU/ODM/UWB) are set to work in parallel under the federated Kalman filter (FKF) framework, which can correct the cumulative error of the internal sensor and and can improve the computational efficiency. Two sets of numerical simulations were performed to show that the proposed method can obtain accurate state estimation with a slightly increased computational burden.

Attitude Determination by Integration of Multi-Sensors Based on Federated Kalman Filter

2014 ◽  
Vol 602-605 ◽  
pp. 2958-2961
Author(s):  
Tao Lai ◽  
Guang Long Wang ◽  
Wen Jie Zhu ◽  
Feng Qi Gao
Keyword(s):  
Kalman Filter ◽  
Inertial Measurement Unit ◽  
Attitude Determination ◽  
Angular Rate ◽  
General Structure ◽  
Test System ◽  
Measurement Unit ◽  
Storage Test ◽  
Inertial Measurement ◽  
Federated Kalman Filter

Micro inertial measurement unit integration storage test system is a typical multi-sensor information fusion system consists of microsensors. The Federated Kalman filter is applied to micro inertial measurement unit integration storage test system. The general structure and characteristics of Federated Kalman filter is expounded. The four-order Runge-Kutta method based on quaternion differential equation was used to dispose the output angular rate data from gyroscope, and the recurrence expressions was established too. The control system based ARM Cortex-M4 master-slave structure is adopted in this paper. The result shown that the dimensionality reduced algorithm significantly reduces implementation complexity of the method and the amount computation. The filtering effect and real-time performance have much increased than traditionally method.

scholarly journals A Robust and Adaptive Complementary Kalman Filter Based on Mahalanobis Distance for Ultra Wideband/Inertial Measurement Unit Fusion Positioning

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3435 ◽  
Author(s):  
Xin Li ◽  
Yan Wang ◽  
Kourosh Khoshelham
Keyword(s):  
Kalman Filter ◽  
Mahalanobis Distance ◽  
Inertial Measurement Unit ◽  
Least Squares Method ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Observation Error ◽  
System State ◽  
Inertial Measurement ◽  
The Impact

Ultra wideband (UWB) has been a popular technology for indoor positioning due to its high accuracy. However, in many indoor application scenarios UWB measurements are influenced by outliers under non-line of sight (NLOS) conditions. To detect and eliminate outlying UWB observations, we propose a UWB/Inertial Measurement Unit (UWB/IMU) fusion filter based on a Complementary Kalman Filter to track the errors of position, velocity and direction. By using the least squares method, the positioning residual of the UWB observation is calculated, the robustness factor of the observation is determined, and an observation weight is dynamically set. When the robustness factor does not exceed a pre-defined threshold, the observed value is considered trusted, and adaptive filtering is used to track the system state, while the abnormity of system state, which might be caused by IMU data exceptions or unreasonable noise settings, is detected by using Mahalanobis distance from the observation to the prior distribution. When the robustness factor exceeds the threshold, the observed value is considered abnormal, and robust filtering is used, whereby the impact of UWB data exceptions on the positioning results is reduced by exploiting Mahalanobis distance. Experimental results show that the observation error can be effectively estimated, and the proposed algorithm can achieve an improved positioning accuracy when affected by outlying system states of different quantity as well as outlying observations of different proportion.

Maximum Correntropy Kalman Filter for Orientation Estimation With Application to LiDAR Inertial Odometry

2020 ◽  
Author(s):  
Seyed Fakoorian ◽  
Matteo Palieri ◽  
Angel Santamaria-Navarro ◽  
Cataldo Guaragnella ◽  
Dan Simon ◽  
...  
Keyword(s):  
Kalman Filtering ◽  
Inertial Measurement Unit ◽  
Low Cost ◽  
Attitude Estimation ◽  
High Rate ◽  
Measurement Unit ◽  
Orientation Estimation ◽  
Localization Accuracy ◽  
Inertial Measurement ◽  
Robotic Applications

Abstract Accurate attitude estimation using low-cost sensors is an important capability to enable many robotic applications. In this paper, we present a method based on the concept of correntropy in Kalman filtering to estimate the 3D orientation of a rigid body using a low-cost inertial measurement unit (IMU). We then leverage the proposed attitude estimation framework to develop a LiDAR-Intertial Odometry (LIO) demonstrating improved localization accuracy with respect to traditional methods. This is of particular importance when the robot undergoes high-rate motions that typically exacerbate the issues associated with low-cost sensors. The proposed orientation estimation approach is first validated using the data coming from a low-cost IMU sensor. We further demonstrate the performance of the proposed LIO solution in a simulated robotic cave exploration scenario.

scholarly journals An Adaptive UWB/MEMS-IMU Complementary Kalman Filter for Indoor Location in NLOS Environment

2019 ◽  
Vol 11 (22) ◽  
pp. 2628 ◽  
Author(s):  
Liu ◽  
Li ◽  
Wang ◽  
Zhang
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Inertial Measurement Unit ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Indoor Location ◽  
Magnetometer Data ◽  
Noise Covariance ◽  
Mems Imu ◽  
Non Line Of Sight

High precision positioning of UWB (ultra-wideband) in NLOS (non-line-of-sight) environment is one of the hot issues in the direction of indoor positioning. In this paper, a method of using a complementary Kalman filter (CKF) to fuse and filter UWB and IMU (inertial measurement unit) data and track the errors of variables such as position, speed, and direction is presented. Based on the uncertainty of magnetometer and acceleration, the noise covariance matrix of magnetometer and accelerometer is calculated dynamically, and then the weight of magnetometer data is set adaptively to correct the directional error of gyroscope. Based on the uncertainty of UWB distance observations, the covariance matrix of UWB measurement noise is calculated dynamically, and then the weight of UWB data observations is set adaptively to correct the position error. The position, velocity and direction errors are corrected by the fusion of UWB and IMU. The experimental results show that the algorithm can reduce the gyroscope deviation with magnetic noise and motion noise, so that the orientation estimates can be improved, as well as the positioning accuracy can be increased with UWB ranging noise.

Global Observability Analysis IMU/Camera Integration

2013 ◽  
Vol 380-384 ◽  
pp. 1069-1072
Author(s):  
Qiang Fang ◽  
Xin Sheng Huang
Keyword(s):  
State Estimation ◽  
Inertial Measurement Unit ◽  
Sufficient Conditions ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Inertial Measurement ◽  
Observability Analysis ◽  
Inertial Measurements

Vision-aided inertial navigation systems can provide precise state estimates for the 3-D motion of a vehicle. This is achieved by combining inertial measurements from an inertial measurement unit (IMU) with visual observations from a camera. Observability is a key aspect of the state estimation problem of INS/Camera. In most previous research, conservative observability concepts based on Lie derivatives have extensively been used to characterize the estimability properties. In this paper, we present a novel approache to investigate the observability of INS/Camera: global observability. The global observability method directly starts from the basic observability definition. The global observability analysis approach is not only straightforward and comprehensive but also provides us with new insights compared with conventional methods. Some sufficient conditions for the global observability of the system is provided.

Localization of a Miniature Spherical Rolling Robot Using IMU, Odometry and UWB

2018 ◽  
Author(s):  
Van Duong Nguyen ◽  
Gim Song Soh ◽  
Shaohui Foong ◽  
Kristin Wood
Keyword(s):  
Inertial Measurement Unit ◽  
Indoor Localization ◽  
Spherical Body ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Spherical Robot ◽  
Intelligent Surveillance ◽  
Localization Performance ◽  
High Degree ◽  
Rolling Robot

Robots that rolls with a spherical body or spherical robots, exhibits a high degree of mobility and amazing recovery capability from collisions while traversing in the environment. However, the localization of spherical robots in a GPS-denied environment for Intelligent Surveillance and Reconnaissance (ISR) task is a challenging problem due to the complexity of its system dynamics and the limited available sensors technology to sense out of the spherical shell. In our prior work, a kinematic localization technique based on odometry and inertial measurement unit (IMU) sensing was proposed and implemented onto our miniature spherical robot Virgo, for pose estimation. However, it suffers from errors due to slippages during locomotion or as a result of the collision. In this paper, we present a solution to this problem by the inclusion of an additional ultra-wideband (UWB) sensor and fuse it with our kinematic pose estimator using Extended Kalman Filter for indoor localization. Experiments are conducted on a multi-waypoint trajectory to verify its validity and had shown to improve localization performance.

Mars Aerobraking Spacecraft State Estimation by Processing Inertial Measurement Unit Data

2008 ◽  
Vol 31 (6) ◽  
pp. 1802-1812 ◽  
Author(s):  
Moriba K. Jah ◽  
Michael E. Lisano ◽  
George H. Born ◽  
Penina Axelrad
Keyword(s):  
State Estimation ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Inertial Measurement
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