Fusion of GPS/INS/Odometer measurements for land vehicle navigation with GPS outage

Aftatah, Mohammed; Lahrech, Abdelkabir; Abounada, Abdelouahed

doi:10.1109/cloudtech.2016.7847724

Cited by 27 publications

(17 citation statements)

References 7 publications

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“…However, the integral of acceleration introduces cumulative errors and finally affects the estimation accuracy. So far, various hybrid positioning methods have been proposed by combining inertial positioning with other methods, such as GPS [17,18], acoustic ranging [19], and Doppler speedometer [20]. e point cloud-based positioning includes a family of methods using kinds of sensors to detect environments around and represent sensing data as a point cloud.…”

Section: Related Workmentioning

confidence: 99%

UWB Base Station Cluster Localization for Unmanned Ground Vehicle Guidance

Ding

et al. 2021

Mathematical Problems in Engineering

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In this paper, we seek to provide unmanned ground vehicles with positioning service using ultrawideband (UWB) technology, a high-accuracy positioning approach. UWB is chosen for two distinct reasons. First, it does not rely on global navigation satellite systems like GPS, making it able to be applied indoors or in an environment where GPS signal is unstable. Second, it is immune to interference from other signals and accurate enough to guide unmanned ground vehicles moving precisely in a complex environment within a narrow road. In this paper, three UWB base stations are aggregated as a group in a 2D space for localization. A large number of tests are performed with a UWB base station cluster in order to validate its positioning performance. Based on the experiment results, we further develop a dynamic particle swarm optimization-based algorithm and a genetic algorithm to deploy multiple clusters of UWB base stations to cover an area of interest. The performance of the proposed algorithms has been tested through a series of simulations. Finally, experiments using unmanned ground vehicles are carried out to validate the localization performance. The results confirm that the robots can follow complex paths accurately with the proposed UWB-based positioning system.

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Section: Related Workmentioning

confidence: 99%

UWB Base Station Cluster Localization for Unmanned Ground Vehicle Guidance

Ding

et al. 2021

Mathematical Problems in Engineering

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“…Once the measurement data are convincing enough, the visual odometry position can be corrected by GPS [10]. Furthermore, the transformation matrix of GPS can be determined to transform the local tangent plane (LTP) coordinates to world coordinates [11]. Within the considerations of the RTK carrier phase ambiguities and the pseudorange multipath errors, the state prediction could be more robust and precise [12].…”

Section: Related Workmentioning

confidence: 99%

Integrating Vehicle Positioning and Path Tracking Practices for an Autonomous Vehicle Prototype in Campus Environment

Yang

Kuo

2021

Electronics

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This paper presents the implementation of an autonomous electric vehicle (EV) project in the National Taiwan University of Science and Technology (NTUST) campus in Taiwan. The aim of this work was to integrate two important practices of realizing an autonomous vehicle in a campus environment, including vehicle positioning and path tracking. Such a project is helpful to the students to learn and practice key technologies of autonomous vehicles conveniently. Therefore, a laboratory-made EV was equipped with real-time kinematic GPS (RTK-GPS) to provide centimeter position accuracy. Furthermore, the model predictive control (MPC) was proposed to perform the path tracking capability. Nevertheless, the RTK-GPS exhibited some robust positioning concerns in practical application, such as a low update rate, signal obstruction, signal drift, and network instability. To solve this problem, a multisensory fusion approach using an unscented Kalman filter (UKF) was utilized to improve the vehicle positioning performance by further considering an inertial measurement unit (IMU) and wheel odometry. On the other hand, the model predictive control (MPC) is usually used to control autonomous EVs. However, the determination of MPC parameters is a challenging task. Hence, reinforcement learning (RL) was utilized to generalize the pre-trained datum value for the determination of MPC parameters in practice. To evaluate the performance of the RL-based MPC, software simulations using MATLAB and a laboratory-made, full-scale electric vehicle were arranged for experiments and validation. In a 199.27 m campus loop path, the estimated travel distance error was 0.82% in terms of UKF. The MPC parameters generated by RL also achieved a better tracking performance with 0.227 m RMSE in path tracking experiments, and they also achieved a better tracking performance when compared to that of human-tuned MPC parameters.

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“…where measurement z m2 additionally uses the difference between the INS velocity v I and the GPS velocity v G . Although the measurement models defined in (13,14) are widely used in various platforms, it is disadvantageous in that it is difficult to estimate the heading error and the corresponding gyro bias when low-cost sensors are used. To solve this problem, the non-holonomic characteristics of the vehicle are used, or the GPS velocity or GPS course measurements provided by the GPS receiver are used.…”

Section: General Gps/ins Integrated Systemmentioning

confidence: 99%

Enhancement of Heading Accuracy for GPS/INS by Employing Average Velocity in Low Dynamic Situations

Choi

Kim

et al. 2020

IEEE Access

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In this paper, we proposed an algorithm that improves the heading accuracy of the global positioning system (GPS)/inertial navigation system (INS) integrated system used in automobiles by manipulating INS velocity and GPS velocity measurements. Two velocities are provided by the GPS receiver: the velocity calculated using the position difference and the velocity calculated using the Doppler shift. The velocity obtained using the position difference is an average velocity for a certain time period, which is inaccurate under dynamic conditions because of its time delay. In contrast, the velocity from the Doppler shift is an instantaneous velocity and has no time delay. However, it also relies on pseudo-range error and noise, which degrades the heading estimation accuracy in low dynamic situations. Thus, although the velocity measurements can improve heading accuracy, the navigation performance is often degraded when the velocity measurements are used for GPS/INS integration. To improve the heading accuracy by solving the aforementioned problems, we proposed a heading accuracy improvement algorithm that employs the average velocity measurements obtained using the averaged GPS velocity and the average velocity of the INS. Since the proposed average velocity measurements are calculated using long baseline, the proposed algorithm can improve the heading accuracy without using other sensors, especially in the case of low dynamic situations. It can be easily applied to the existing GPS/INS integrated system, making it suitable for use in automotive navigation systems. In this research, it is verified that the average velocity measurements can be substituted for Kalman filter measurements, and the performance improvements are confirmed through simulations and experiments. INDEX TERMS Automobile vehicles navigation, GPS integrated navigation, velocity, heading accuracy, average velocity measurements.

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Fusion of GPS/INS/Odometer measurements for land vehicle navigation with GPS outage

Cited by 27 publications

References 7 publications

UWB Base Station Cluster Localization for Unmanned Ground Vehicle Guidance

UWB Base Station Cluster Localization for Unmanned Ground Vehicle Guidance

Integrating Vehicle Positioning and Path Tracking Practices for an Autonomous Vehicle Prototype in Campus Environment

Enhancement of Heading Accuracy for GPS/INS by Employing Average Velocity in Low Dynamic Situations

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