Sensor Fusion For Land Vehicle Localization Using Inertial MEMS and Odometry

Mikov, Aleksandr; Panyov, A.A.; Kosyanchuk, Vasily; Prikhodko, Igor P.

doi:10.1109/isiss.2019.8739427

Cited by 22 publications

(7 citation statements)

References 3 publications

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“…In general, accumulative error is a critical factor of odometry. Hence, Mikov et al [14] integrated inertial MEMS (Micro-Electromechanical Systems) and odometry solutions for land vehicle localizations. When the GNSS (Global Navigation Satellite System) signal is available, it can become the odometry correction factor to fix the position.…”

Section: Related Workmentioning

confidence: 99%

“…The UKF is briefly introduced [1]. Assume that there is a discretetime, nonlinear system, as in Equation (14).…”

Section: Ukf-based Position Estimationmentioning

confidence: 99%

“…Table 2 also indicates that the UKF represented the minimum error, mean error, and standard deviation when compared to odometry and RTK. Moreover, Table 3 shows a performance comparison with the state-of-the-art methods [10,14]. Our purpose was to know the performance of our UKF localization.…”

Section: Integrated Experiments With Ev By Applying Rl-based Mpcmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

“…The UKF is briefly introduced [1]. Assume that there is a discretetime, nonlinear system, as in Equation (14).…”

Section: Ukf-based Position Estimationmentioning

confidence: 99%

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

Yang

Kuo

2021

Electronics

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“…The challenge, therefore, becomes one of accurately predicting the position of the vehicle in the absence of the GNSS signal needed for positioning and correction. Traditionally, Kalman filters are used in modelling the error between the Global Positioning System (GPS) and INS positions [6][7][8]. However, they have limitations when modelling highly non-linear dependencies, stochastic relationships and non-Gaussian noise measurements [6].…”

Section: Introductionmentioning

confidence: 99%

Learning to Localise Automated Vehicles in Challenging Environments Using Inertial Navigation Systems (INS)

2021

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An approach based on Artificial Neural Networks is proposed in this paper to improve the localisation accuracy of Inertial Navigation Systems (INS)/Global Navigation Satellite System (GNSS) based aided navigation during the absence of GNSS signals. The INS can be used to continuously position autonomous vehicles during GNSS signal losses around urban canyons, bridges, tunnels and trees, however, it suffers from unbounded exponential error drifts cascaded over time during the multiple integrations of the accelerometer and gyroscope measurements to position. More so, the error drift is characterised by a pattern dependent on time. This paper proposes several efficient neural network-based solutions to estimate the error drifts using Recurrent Neural Networks, such as the Input Delay Neural Network (IDNN), Long Short-Term Memory (LSTM), Vanilla Recurrent Neural Network (vRNN), and Gated Recurrent Unit (GRU). In contrast to previous papers published in literature, which focused on travel routes that do not take complex driving scenarios into consideration, this paper investigates the performance of the proposed methods on challenging scenarios, such as hard brake, roundabouts, sharp cornering, successive left and right turns and quick changes in vehicular acceleration across numerous test sequences. The results obtained show that the Neural Network-based approaches are able to provide up to 89.55% improvement on the INS displacement estimation and 93.35% on the INS orientation rate estimation.

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“…These solutions traditionally utilize data from available enabling technologies to estimate the position of mobile devices or users in the environment. These enabling technologies can be represented by ultrasound [ 2 ], cameras [ 3 ], light sensors [ 4 ], magnetometers [ 5 ], MEMS (Micro-Electro-Mechanical Systems) or IMUs (Inertial Measurement Units) [ 6 , 7 , 8 ] as well as radio receivers [ 9 ]. Each of these technologies has its pros and cons.…”

Section: Introductionmentioning

confidence: 99%

Algorithm for Dynamic Fingerprinting Radio Map Creation Using IMU Measurements

Brída

Machaj

Racko

et al. 2021

Sensors

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While a vast number of location-based services appeared lately, indoor positioning solutions are developed to provide reliable position information in environments where traditionally used satellite-based positioning systems cannot provide access to accurate position estimates. Indoor positioning systems can be based on many technologies; however, radio networks and more precisely Wi-Fi networks seem to attract the attention of a majority of the research teams. The most widely used localization approach used in Wi-Fi-based systems is based on fingerprinting framework. Fingerprinting algorithms, however, require a radio map for position estimation. This paper will describe a solution for dynamic radio map creation, which is aimed to reduce the time required to build a radio map. The proposed solution is using measurements from IMUs (Inertial Measurement Units), which are processed with a particle filter dead reckoning algorithm. Reference points (RPs) generated by the implemented dead reckoning algorithm are then processed by the proposed reference point merging algorithm, in order to optimize the radio map size and merge similar RPs. The proposed solution was tested in a real-world environment and evaluated by the implementation of deterministic fingerprinting positioning algorithms, and the achieved results were compared with results achieved with a static radio map. The achieved results presented in the paper show that positioning algorithms achieved similar accuracy even with a dynamic map with a low density of reference points.

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Sensor Fusion For Land Vehicle Localization Using Inertial MEMS and Odometry

Cited by 22 publications

References 3 publications

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

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

Learning to Localise Automated Vehicles in Challenging Environments Using Inertial Navigation Systems (INS)

Algorithm for Dynamic Fingerprinting Radio Map Creation Using IMU Measurements

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