scholarly journals Tight Fusion of a Monocular Camera, MEMS-IMU, and Single-Frequency Multi-GNSS RTK for Precise Navigation in GNSS-Challenged Environments

2019 ◽  
Vol 11 (6) ◽  
pp. 610 ◽  
Author(s):  
Tuan Li ◽  
Hongping Zhang ◽  
Zhouzheng Gao ◽  
Xiaoji Niu ◽  
Naser El-sheimy
Keyword(s):  
Kalman Filter ◽  
Low Cost ◽  
Measurement Model ◽  
Satellite System ◽  
Single Frequency ◽  
Precise Position ◽  
Monocular Camera ◽  
Tightly Coupled ◽  
Self Driving Cars ◽  
Mems Imu

Precise position, velocity, and attitude is essential for self-driving cars and unmanned aerial vehicles (UAVs). The integration of global navigation satellite system (GNSS) real-time kinematics (RTK) and inertial measurement units (IMUs) is able to provide high-accuracy navigation solutions in open-sky conditions, but the accuracy will be degraded severely in GNSS-challenged environments, especially integrated with the low-cost microelectromechanical system (MEMS) IMUs. In order to navigate in GNSS-denied environments, the visual–inertial system has been widely adopted due to its complementary characteristics, but it suffers from error accumulation. In this contribution, we tightly integrate the raw measurements from the single-frequency multi-GNSS RTK, MEMS-IMU, and monocular camera through the extended Kalman filter (EKF) to enhance the navigation performance in terms of accuracy, continuity, and availability. The visual measurement model from the well-known multistate constraint Kalman filter (MSCKF) is combined with the double-differenced GNSS measurement model to update the integration filter. A field vehicular experiment was carried out in GNSS-challenged environments to evaluate the performance of the proposed algorithm. Results indicate that both multi-GNSS and vision contribute significantly to the centimeter-level positioning availability in GNSS-challenged environments. Meanwhile, the velocity and attitude accuracy can be greatly improved by using the tightly-coupled multi-GNSS RTK/INS/Vision integration, especially for the yaw angle.

scholarly journals Implementation and Analysis of Tightly Coupled Global Navigation Satellite System Precise Point Positioning/Inertial Navigation System (GNSS PPP/INS) with Insufficient Satellites for Land Vehicle Navigation

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4305 ◽  
Author(s):  
Yue Liu ◽  
Fei Liu ◽  
Yang Gao ◽  
Lin Zhao
Keyword(s):  
Low Cost ◽  
Satellite System ◽  
Single Frequency ◽  
Vehicle Navigation ◽  
Land Vehicle ◽  
Land Vehicle Navigation ◽  
Gps Satellites ◽  
Tightly Coupled ◽  
Gnss Measurements ◽  
Global Navigation Satellite

This paper implements and analyzes a tightly coupled single-frequency global navigation satellite system precise point positioning/inertial navigation system (GNSS PPP/INS) with insufficient satellites for land vehicle navigation using a low-cost GNSS receiver and a microelectromechanical system (MEMS)-based inertial measurement unit (IMU). For land vehicle navigation, it is inevitable to encounter the situation where insufficient satellites can be observed. Therefore, it is necessary to analyze the performance of tightly coupled integration in a GNSS-challenging environment. In addition, it is also of importance to investigate the least number of satellites adopted to improve the performance, compared with no satellites used. In this paper, tightly coupled integration using low-cost sensors with insufficient satellites was conducted, which provided a clear view of the improvement of the solution with insufficient satellites compared to no GNSS measurements at all. Specifically, in this paper single-frequency PPP was implemented to achieve the best performance, with one single-frequency receiver. The INS mechanization was conducted in a local-level frame (LLF). An extended Kalman filter was applied to fuse the two different types of measurements. To be more specific, in PPP processing, the atmosphere errors are corrected using a Saastamoinen model and the Center for Orbit Determination in Europe (CODE) global ionosphere map (GIM) product. The residuals of atmosphere errors are not estimated to accelerate the ambiguity convergence. For INS error mitigation, velocity constraints for land vehicle navigation are adopted to limit the quick drift of a MEMS-based IMU. Field tests with simulated partial and full GNSS outages were conducted to show the performance of tightly coupled GNSS PPP/INS with insufficient satellites: The results were classified as long-term (several minutes) and short-term (less than 1 min). The results showed that generally, with GNSS measurements applied, although the number of satellites was not enough, the solution still could be improved, especially with more than three satellites observed. With three GPS satellites used, the horizontal drift could be reduced to a few meters after several minutes. The 3D position error could be limited within 10 m in one minute when three GPS satellites were applied. In addition, a field test in an urban area where insufficient satellites were observed from time to time was also conducted to show the limited solution drift.

scholarly journals Kinematic PPP Using Mixed GPS/Glonass Single-Frequency Observations

2019 ◽  
Vol 54 (3) ◽  
pp. 97-112
Author(s):  
Mostafa Hamed ◽  
Ashraf Abdallah ◽  
Ashraf Farah
Keyword(s):  
Low Cost ◽  
Satellite System ◽  
Single Frequency ◽  
Future Research ◽  
Frequency Data ◽  
Dual Frequency ◽  
Trajectory Data ◽  
Average Improvement ◽  
Kinematic Ppp ◽  
Global Navigation Satellite

Abstract Nowadays, Precise Point Positioning (PPP) is a very popular technique for Global Navigation Satellite System (GNSS) positioning. The advantage of PPP is its low cost as well as no distance limitation when compared with the differential technique. Single-frequency receivers have the advantage of cost effectiveness when compared with the expensive dual-frequency receivers, but the ionosphere error makes a difficulty to be completely mitigated. This research aims to assess the effect of using observations from both GPS and GLONASS constellations in comparison with GPS only for kinematic purposes using single-frequency observations. Six days of the year 2018 with single-frequency data for the Ethiopian IGS station named “ADIS” were processed epoch by epoch for 24 hours once with GPS-only observations and another with GPS/GLONASS observations. In addition to “ADIS” station, a kinematic track in the New Aswan City, Aswan, Egypt, has been observed using Leica GS15, geodetic type, dual-frequency, GPS/GLONASS GNSS receiver and single-frequency data have been processed. Net_Diff software was used for processing all the data. The results have been compared with a reference solution. Adding GLONASS satellites significantly improved the satellite number and Position Dilution Of Precision (PDOP) value and accordingly improved the accuracy of positioning. In the case of “ADIS” data, the 3D Root Mean Square Error (RMSE) ranged between 0.273 and 0.816 m for GPS only and improved to a range from 0.256 to 0.550 m for GPS/GLONASS for the 6 processed days. An average improvement ratio of 24%, 29%, 30%, and 29% in the east, north, height, and 3D position components, respectively, was achieved. For the kinematic trajectory, the 3D position RMSE improved from 0.733 m for GPS only to 0.638 m for GPS/GLONASS. The improvement ratios were 7%, 5%, 28%, and 13% in the east, north, height, and 3D position components, respectively, for the kinematic trajectory data. This opens the way to add observations from the other two constellations (Galileo and BeiDou) for more accuracy in future research.

scholarly journals PERFORMANCE ASSESSMENT OF INTEGRATED SENSOR ORIENTATION WITH A LOW-COST GNSS RECEIVER

2017 ◽  
Vol IV-2/W3 ◽  
pp. 75-80 ◽  
Author(s):  
M. Rehak ◽  
J. Skaloud
Keyword(s):  
Position Control ◽  
Low Cost ◽  
Satellite System ◽  
Bundle Adjustment ◽  
Single Frequency ◽  
Reference Trajectory ◽  
Integrated Sensor ◽  
Homogeneous Surface ◽  
Gnss Receiver ◽  
Sensor Orientation

Mapping with Micro Aerial Vehicles (MAVs whose weight does not exceed 5&amp;thinsp;kg) is gaining importance in applications such as corridor mapping, road and pipeline inspections, or mapping of large areas with homogeneous surface structure, e.g. forest or agricultural fields. In these challenging scenarios, integrated sensor orientation (ISO) improves effectiveness and accuracy. Furthermore, in block geometry configurations, this mode of operation allows mapping without ground control points (GCPs). Accurate camera positions are traditionally determined by carrier-phase GNSS (Global Navigation Satellite System) positioning. However, such mode of positioning has strong requirements on receiver’s and antenna’s performance. In this article, we present a mapping project in which we employ a single-frequency, low-cost (<&amp;thinsp;$100) GNSS receiver on a MAV. The performance of the low-cost receiver is assessed by comparing its trajectory with a reference trajectory obtained by a survey-grade, multi-frequency GNSS receiver. In addition, the camera positions derived from these two trajectories are used as observations in bundle adjustment (BA) projects and mapping accuracy is evaluated at check points (ChP). Several BA scenarios are considered with absolute and relative aerial position control. Additionally, the presented experiments show the possibility of BA to determine a camera-antenna spatial offset, so-called lever-arm.

scholarly journals Adaptive Filtering on GPS-Aided MEMS-IMU for Optimal Estimation of Ground Vehicle Trajectory

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5357 ◽  
Author(s):  
Haseeb Ahmed ◽  
Ihsan Ullah ◽  
Uzair Khan ◽  
Muhammad Bilal Qureshi ◽  
Sajjad Manzoor ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Autonomous Navigation ◽  
Inertial Sensors ◽  
Low Cost ◽  
Optimal Estimation ◽  
Cost Effective ◽  
Estimation Accuracy ◽  
Ground Vehicle ◽  
Navigation Data ◽  
Mems Imu

Fusion of the Global Positioning System (GPS) and Inertial Navigation System (INS) for navigation of ground vehicles is an extensively researched topic for military and civilian applications. Micro-electro-mechanical-systems-based inertial measurement units (MEMS-IMU) are being widely used in numerous commercial applications due to their low cost; however, they are characterized by relatively poor accuracy when compared with more expensive counterparts. With a sudden boom in research and development of autonomous navigation technology for consumer vehicles, the need to enhance estimation accuracy and reliability has become critical, while aiming to deliver a cost-effective solution. Optimal fusion of commercially available, low-cost MEMS-IMU and the GPS may provide one such solution. Different variants of the Kalman filter have been proposed and implemented for integration of the GPS and the INS. This paper proposes a framework for the fusion of adaptive Kalman filters, based on Sage-Husa and strong tracking filtering algorithms, implemented on MEMS-IMU and the GPS for the case of a ground vehicle. The error models of the inertial sensors have also been implemented to achieve reliable and accurate estimations. Simulations have been carried out on actual navigation data from a test vehicle. Measurements were obtained using commercially available GPS receiver and MEMS-IMU. The solution was shown to enhance navigation accuracy when compared to conventional Kalman filter.

scholarly journals Performance Enhancement of Pedestrian Navigation Systems Based on Low-Cost Foot-Mounted MEMS-IMU/Ultrasonic Sensor

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 364 ◽  
Author(s):  
Ming Xia ◽  
Chundi Xiu ◽  
Dongkai Yang ◽  
Li Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Low Cost ◽  
Ultrasonic Sensor ◽  
Measurement Unit ◽  
Positioning Error ◽  
Process Noise ◽  
Pedestrian Navigation ◽  
Mems Imu

The pedestrian navigation system (PNS) based on inertial navigation system-extended Kalman filter-zero velocity update (INS-EKF-ZUPT or IEZ) is widely used in complex environments without external infrastructure owing to its characteristics of autonomy and continuity. IEZ, however, suffers from performance degradation caused by the dynamic change of process noise statistics and heading estimation errors. The main goal of this study is to effectively improve the accuracy and robustness of pedestrian localization based on the integration of the low-cost foot-mounted microelectromechanical system inertial measurement unit (MEMS-IMU) and ultrasonic sensor. The proposed solution has two main components: (1) the fuzzy inference system (FIS) is exploited to generate the adaptive factor for extended Kalman filter (EKF) after addressing the mismatch between statistical sample covariance of innovation and the theoretical one, and the fuzzy adaptive EKF (FAEKF) based on the MEMS-IMU/ultrasonic sensor for pedestrians was proposed. Accordingly, the adaptive factor is applied to correct process noise covariance that accurately reflects previous state estimations. (2) A straight motion heading update (SMHU) algorithm is developed to detect whether a straight walk happens and to revise errors in heading if the ultrasonic sensor detects the distance between the foot and reflection point of the wall. The experimental results show that horizontal positioning error is less than 2% of the total travelled distance (TTD) in different environments, which is the same order of positioning error compared with other works using high-end MEMS-IMU. It is concluded that the proposed approach can achieve high performance for PNS in terms of accuracy and robustness.

scholarly journals Robust INS/SFPPP-BDS tightly coupled navigation algorithm with adaptive cycle slip compensation model

2019 ◽  
Vol 13 ◽  
pp. 174830181983304
Author(s):  
Hangshuai Ma ◽  
Rong Wang ◽  
Zhi Xiong ◽  
Jianye Liu ◽  
Chuanyi Li
Keyword(s):  
Navigation System ◽  
Carrier Phase ◽  
Low Cost ◽  
Integrated System ◽  
Single Frequency ◽  
Cycle Slip ◽  
Phase Measurements ◽  
Satellite Navigation System ◽  
Tightly Coupled ◽  
Robust Adaptive

The application of Beidou Satellite Navigation System (BDS) is developing rapidly. To satisfy the increasing demand for positioning performance, single-frequency precise point positioning (SFPPP) has been a focus in recent years. By introducing the SFPPP technique into the INS/BDS integrated system, higher navigation accuracy can be obtained. Cycle slip, which is caused by signal blockage during the measurement of the carrier phase, is a challenge for SFPPP application. In the INS/SFPPP-BDS integrated system, cycle slip can cause serious bias in BDS carrier phase measurements. In this paper, a new INS/SFBDS-PPP tightly coupled navigation system and a robust adaptive filtering method are proposed. Using a low-cost single-frequency receiver integrated with INS, an observation model was built based on the pseudo range and carrier phase by PPP preprocessing. The cycle slip was introduced into the state vector to improve the estimation precision. The test statistics, comprising the innovation and its covariance, were used to estimate the time at which cycle slip occurred and its amplitude to compensate for its effect on the observation. Finally, the proposed system model and algorithm are validated by simulation.

scholarly journals An Adaptive Low-Cost GNSS/MEMS-IMU Tightly-Coupled Integration System with Aiding Measurement in a GNSS Signal-Challenged Environment

Sensors ◽  
2015 ◽  
Vol 15 (9) ◽  
pp. 23953-23982 ◽  
Author(s):  
Qifan Zhou ◽  
Hai Zhang ◽  
You Li ◽  
Zheng Li
Keyword(s):  
Low Cost ◽  
Integration System ◽  
Tightly Coupled ◽  
Mems Imu

MEMS IMU and GPS/Beidou Integration Navigation System Using Interval Kalman Filter

2014 ◽  
Vol 568-570 ◽  
pp. 970-975 ◽  
Author(s):  
Yang Le ◽  
Xiu Feng He ◽  
Ru Ya Xiao
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Low Cost ◽  
Integrated System ◽  
Integrated Navigation ◽  
Dynamics Modeling ◽  
Integrated Navigation System ◽  
Uncertain Dynamics ◽  
Integration Navigation ◽  
Mems Imu

This paper describes an integrated MEMS IMU and GNSS system for vehicles. The GNSS system is developed to accurately estimate the vehicle azimuth, and the integrated GNSS/IMU provides attitude, position and velocity. This research is aimed at producing a low-cost integrated navigation system for vehicles. Thus, an inexpensive solid-state MEMS IMU is used to smooth the noise and to provide a high bandwidth response. The integration system with uncertain dynamics modeling and uncertain measurement noise is also studied. An interval adaptive Kalman filter is developed for such an uncertain integrated system, since the standard extended Kalman filter (SKF) is no longer applicable, and a method of adaptive factor construction with uncertain parameter is proposed for the nonlinear integrated GNSS/IMU system. The results from the actual GNSS/IMU integrated system indicate that the filtering method proposed is effective.

A Low Cost Attitude and Heading Reference System Based on a MEMS IMU for a T-Quadrotor

2011 ◽  
Author(s):  
Yangbo Long ◽  
Shi Bai ◽  
Paras Patel ◽  
David J. Cappelleri
Keyword(s):  
Kalman Filter ◽  
Reference System ◽  
Low Cost ◽  
Magnetic Sensors ◽  
Estimation Errors ◽  
Highly Nonlinear ◽  
Mems Gyroscopes ◽  
Vector Algorithms ◽  
Mems Imu ◽  
Acquisition Method

Combining signals from accelerometers and gyroscopes is a widely used way to estimate robot attitude. However, when using a Kalman filter in this case, the measurements are vulnerable to dynamic accelerations which will result in substantial attitude estimation errors. The attitude acquisition method presented in this paper takes an attitude quaternion as system measurements and uses a Kalman filter to fuse signals from MEMS gyroscopes, accelerometers and magnetic sensors. In order to remove the influence of dynamic accelerations, when dynamic accelerations are found to be significant, a Quaternion-based Strapdown Navigation System (Q-SINS) algorithm is only applied without the Kalman filtering. When the dynamic accelerations are not significant, both the Q-SINS and the Bi-vector algorithms are utilized and fused using the Kalman filter for improved system performance. Compared with some other highly nonlinear and complicated attitude algorithms, the Attitude and Heading Reference System (AHRS) proposed in this paper is computationally less expensive and more suitable for real-time applications.

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