Figure 1 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Source publication
Microelectromechanical Systems (MEMS) Inertial Measurement Unit (IMU) containing a three-orthogonal gyroscope and three-orthogonal accelerometer has been widely utilized in position and navigation, due to gradually improved accuracy and its small size and low cost. However, the errors of a MEMS IMU based standalone Inertial Navigation System (INS)...
Contexts in source publication
Context 1
... a LSTM unit is composed of a cell, an "input gate", "output gate" and a "forget gate." The basic structure of a single layer LSTM unit is shown as Figure 1. The cell remembers values over arbitrary time intervals and the three different gates regulate and control the flow of information into and out of the cell [38][39][40]. ...
Context 2
... is the detailed description of the different gates and relative equations. As illustrated in Figure 1, the first part of the LSTM is the "forget gate", which is employed to decide what information is going to get thrown away from the cell state, the decision is made by a sigmoid layer called "forget gate layer". of each number in the cell state, and "1" represents "completely keep this" while "0" represents "completely get rid of this". The operation equation t f is as: ...
Similar publications
In order to improve the performance of a micro-electro-mechanical system (MEMS) accelerometer, three algorithms for compensating its temperature drift are proposed in this paper, including deep long short-term memory recurrent neural network (DLSTM-RNN, short DLSTM), DLSTM based on sparrow search algorithm (SSA), and DLSTM based on improved SSA (IS...
Citations
... In the integrated navigation system, the primary navigation system operates continuously. Concurrently, other navigation systems provide correction data to enhance the positioning accuracy of the system and continuously supply navigation parameters [1][2][3][4]. The most common combination for integrated navigation systems is the employment of SINS as the primary navigation system, with the GPS serving as the auxiliary navigation system. ...
In order to reduce the position errors of the Global Positioning System/Strapdown Inertial Navigation System (GPS/SINS) integrated navigation system during GPS denial, this paper proposes a method based on the Particle Swarm Optimization–Back Propagation Neural Network (PSO-BPNN) to replace the GPS for positioning. The model relates the position information, velocity information, attitude information output by the SINS, and the navigation time to the position errors between the position information output by the SINS and the actual position information. The performance of the model is compared with the BPNN through an actual ship experiment. The results show that the PSO-BPNN can obviously reduce the position errors in the case of GPS signal denial.
... Therefore, long short-term memory (LSTM) and gated recurrent unit (GRU) are two improved RNN algorithms developed to solve these problems [17], [18]. Jiang [19] et al. used LSTM to denoise the output signals of a MEMS gyroscope, and the results show that this method can effectively improve the accuracy of the device. However, the model test only uses two minutes of static data from the gyroscope, which lacks certain robustness. ...
... To characterize the difference between the estimated quaternion and the true quaternion, we measure the distance between two quaternions by calculating the inner product UQD of these two quaternions according to [32], as shown in (19). ...
In recent years, due to the proliferation of inertial measurement units (IMUs) in mobile devices such as smartphones, attitude estimation using inertial and magnetic sensors has been the subject of considerable research. Traditional methods involve probabilistic and iterative state estimation; however, these approaches do not generalize well over continuously changing motion dynamics and environmental conditions. Therefore, this paper proposes a deep learning-based approach for attitude estimation. This approach segments data from sensors into different windows and estimates attitude by separately extracting local features and global features from sensor data using a residual network (ResNet18) and a long short-term memory network (LSTM). To improve the accuracy of attitude estimation, a multi-scale attention mechanism is designed within ResNet18 to capture finer temporal information in the sensor data. The experimental results indicate that the accuracy of attitude estimation using this method surpasses that of other methods proposed in recent years.
... With traditional inertial navigation methods approaching developmental thresholds, the integration of neural networks with inertial navigation has recently become a focal point of research. Chang et al. [11] proposed a method using Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs) for denoising Microelectromechanical Systems (MEMSs) IMU. Martin Brossard et al. [12] introduced a learning method for denoising IMU gyroscopes using real-world data. ...
The pure inertial navigation system, crucial for autonomous navigation in GPS-denied environments, faces challenges of error accumulation over time, impacting its effectiveness for prolonged missions. Traditional methods to enhance accuracy have focused on improving instrumentation and algorithms but face limitations due to complexity and costs. This study introduces a novel device-level redundant inertial navigation framework using high-precision accelerometers combined with a neural network-based method to refine navigation accuracy. Experimental validation confirms that this integration significantly boosts navigational precision, outperforming conventional system-level redundancy approaches. The proposed method utilizes the advanced capabilities of high-precision accelerometers and deep learning to achieve superior predictive accuracy and error reduction. This research paves the way for the future integration of cutting-edge technologies like high-precision optomechanical and atom interferometer accelerometers, offering new directions for advanced inertial navigation systems and enhancing their application scope in challenging environments.
... Usually, the manufacturer corrects the systematic errors in the IMU chip when leaving the factory [21]. As for random errors, various data fusion technologies have been developed to overcome them [22][23][24]. ...
... Sensors 2023, 23, 9468 Acknowledgments: The authors would like to acknowledge the anonymous reviewers and editors whose thoughtful comments helped to improve this manuscript. ...
Visual simultaneous localization and mapping is a widely used technology for mobile robots to carry out precise positioning in the environment of GNSS technology failure. However, as the robot moves around indoors, its position accuracy will gradually decrease over time due to common and unavoidable environmental factors. In this paper, we propose an improved method called RTABMAP-VIWO, which is based on RTABMAP. The basic idea is to use an Extended Kalman Filter (EKF) framework for fusion attitude estimates from the wheel odometry and IMU, and provide new prediction values. This helps to reduce the local cumulative error of RTABMAP and make it more accurate. We compare and evaluate three kinds of SLAM methods using both public datasets and real indoor scenes. In the dataset experiments, our proposed method reduces the Root-Mean-Square Error (RMSE) coefficient by 48.1% compared to the RTABMAP, and the coefficient is also reduced by at least 29.4% in the real environment experiments. The results demonstrate that the improved method is feasible. By incorporating the IMU into the RTABMAP method, the trajectory and posture errors of the mobile robot are significantly improved.
... Digital signal processing includes methods such as wavelet denoising [4], empirical mode decomposition (EMD) [5], variational mode decomposition (VMD) [6], and filtering. The main modeling methods include time series modeling [7] and artificial intelligence (AI) modeling [8]. Wavelet denoising often requires the selection of an appropriate wavelet basis, decomposition level, threshold, and threshold function [9][10][11]. ...
The random error in Micro-Electro-Mechanical Systems (MEMS) gyroscopes is one of the major aspects that limit the measurement accuracy. In order to address the inaccurate extraction of noise and trend during the signal preprocessing, as well as the subjectivity in auto regressive moving average (ARMA) model ordering, this paper proposes a method based on interval empirical mode decomposition (IEMD) and ARMA model. In the proposed method, the original signal is decomposed into a series of intrinsic mode functions (IMFs) and a residual through empirical mode decomposition (EMD). Based on the Hellinger distance and autocorrelation function, IMFs are then classified into noise IMFs, hybrid IMFs, and signal IMFs. The improved sand cat swarm optimization (SCSO) is utilized to optimize the ordering process of the ARMA model. The improved adaptive filter is adopted to compensate the random error, and the compensated signal is reconstructed with the signal IMFs and residual to obtain the final output. Experiments show that under static conditions, the proposed method could reduce the root mean square error by 52.6% and 33.3%, respectively, compared with the traditional EMD and ARMA methods. Under dynamic conditions, the proposed method could reduce the root mean square error by 51.1% and 37.1%, respectively, compared with the traditional EMD and ARMA methods. The proposed method could effectively compensate the random error and improve the measurement accuracy of MEMS gyroscopes.
... In the last several years, influenced by the successful utilization of deep learning in various applications, methods using deep learning for orientation estimation have been proposed [15][16][17]. Specifically, instead of conventional filter algorithms, alternative approaches have been developed by training neural networks (NNs) end-to-end with a large number of datasets, including raw nine-axis IMU signals and ground truth orientation data [18][19][20][21][22][23]. ...
The nine-axis inertial and measurement unit (IMU)-based three-dimensional (3D) orientation estimation is a fundamental part of inertial motion capture. Recently, owing to the successful utilization of deep learning in various applications, orientation estimation neural networks (NNs) trained on large datasets, including nine-axis IMU signals and reference orientation data, have been developed. During the training process, the limited amount of training data is a critical issue in the development of powerful networks. Data augmentation, which increases the amount of training data, is a key approach for addressing the data shortage problem and thus for improving the estimation performance. However, to the best of our knowledge, no studies have been conducted to analyze the effects of data augmentation techniques on estimation performance in orientation estimation networks using IMU sensors. This paper selects three data augmentation techniques for IMU-based orientation estimation NNs, i.e., augmentation by virtual rotation, bias addition, and noise addition (which are hereafter referred to as rotation, bias, and noise, respectively). Then, this paper analyzes the effects of these augmentation techniques on estimation accuracy in recurrent neural networks, for a total of seven combinations (i.e., rotation only, bias only, noise only, rotation and bias, rotation and noise, and rotation and bias and noise). The evaluation results show that, among a total of seven augmentation cases, four cases including ‘rotation’ (i.e., rotation only, rotation and bias, rotation and noise, and rotation and bias and noise) occupy the top four. Therefore, it may be concluded that the augmentation effect of rotation is overwhelming compared to those of bias and noise. By applying rotation augmentation, the performance of the NN can be significantly improved. The analysis of the effect of the data augmentation techniques presented in this paper may provide insights for developing robust IMU-based orientation estimation networks.
... Recently, some works have started to use deep learning to estimate attitude. Jiang et al [21] proposed an artificial intelligence method for denoising MEMS-IMU output signals. In other words, the signals are filtered by using LSTM. ...
The recent research shows that data-driven inertia navigation technology can significantly alleviate the drift error of Micro-Electro-Mechanical System Inertial Measurement Unit (MEMS-IMU) in pedestrian localization. However, most existing methods must rely on attitude information provided by external procedure (such as smartphone API), which violates the original intention of full autonomy of inertial navigation, and attitude information is also inaccurate. To address the problem, we propose a pedestrian indoor neural inertial navigation system that does not rely on external information and is only based on low-cost MEMS-IMU. First, a deep learning based neural inertial network was designed to estimate attitude. Then, in order to obtain position estimation with both global and local accuracy, an Invariant Extended Kalman Filter (IEKF) framework was proposed, where 3D displacement and its uncertainty regressed by a deep residual network are utilized to update IEKF. Extensive experimental results on a public dataset and a self-collected dataset show that the proposed method provides accurate attitude estimation and outperforms state-of-the-art methods in position estimation, demonstrating the superiority of our method in reliability and accuracy.
... With supervised methods, we need true data to train the neural network model. Changhui Jiang et al. [8], based on supervised learning, used a combination of recursive neural network (RNN) and long and short time memory network (LSTM) to process the output of inertial sensor as a time series signal, finally improving the accuracy of the inertial sensor. Despite the wide application of supervised learning, its limitations are evident, as it is difficult to acquire data that are close to true values and the accuracy of the true value samples severely affects the effectiveness of denoising algorithms. ...
Onboard electrostatic suspension inertial sensors are important applications for gravity satellites and space gravitational-wave detection missions, and it is important to suppress noise in the measurement signal. Due to the complex coupling between the working space environment and the satellite platform, the process of noise generation is extremely complex, and traditional noise modeling and subtraction methods have certain limitations. With the development of deep learning, applying it to high-precision inertial sensors to improve the signal-to-noise ratio is a practically meaningful task. Since there is a single noise sample and unknown true value in the measured data in orbit, odd–even sub-samplers and periodic sub-samplers are designed to process general signals and periodic signals, and adds reconstruction layers consisting of fully connected layers to the model. Experimental analysis and comparison are conducted based on simulation data, GRACE-FO acceleration data, and Taiji-1 acceleration data. The results show that the deep learning method is superior to traditional data smoothing processing solutions.
... Apart from the papers mentioned above, the majority of research focuses on the denoising and calibration of gyroscopes. A series of papers [111,112,113] examined the denoising of gyroscope data by utilizing various variations of RNNs. One paper demonstrated the performance of a simple RNN structure, while the others utilized LSTMs. ...
Inertial sensing is used in many applications and platforms, ranging from day-to-day devices such as smartphones to very complex ones such as autonomous vehicles. In recent years, the development of machine learning and deep learning techniques has increased significantly in the field of inertial sensing. This is due to the development of efficient computing hardware and the accessibility of publicly available sensor data. These data-driven approaches are used to empower model-based navigation and sensor fusion algorithms. This paper provides an in-depth review of those deep learning methods. We examine separately, each vehicle operation domain including land, air, and sea. Each domain is divided into pure inertial advances and improvements based on filter parameters learning. In addition, we review deep learning approaches for calibrating and denoising inertial sensors. Throughout the paper, we discuss these trends and future directions. We also provide statistics on the commonly used approaches to illustrate their efficiency and stimulate further research in deep learning embedded in inertial navigation and fusion.
... for four categories of noisy tasks, including additive white noisy images, blind denoising, real noisy images, and hybrid noisy images, and analyzed the motivations and principles of the different types of these methods. The authors also compared the state-of-the-art methods on public denoising datasets using quantitative and qualitative analysis. Jiang et. al. Jiang et al. (2018) used LSTM network to filter MEMS-based gyroscope outputs to improve the accuracy of standalone INS. The results indicate that the denoising scheme was effective to improve MEMS INS accuracy, and the proposed LSTM method was preferable in this application. This study also compares LSTM with Auto-Regressive and Moving Average (ARMA) model ...
This review article presents recent advancements in deep learning method-ologies and applications for autonomous navigation. It analyzes state-of-the-art deep learning frameworks used in tasks like signal processing, attitude estimation, obstacle detection, scene perception, and path planning. The implementation and testing methodologies of these approaches are critically evaluated, highlighting their strengths, limitations, and areas for further development. The review emphasizes the interdisciplinary nature of autonomous navigation and addresses challenges posed by dynamic and complex environments, uncertainty, and obstacles. With a particular focus on mobile robots, self-driving cars, unmanned aerial vehicles, and space vehicles to underscore the importance of navigation in these domains. By synthesizing findings from multiple studies, the review aims to be a valuable resource for researchers and practitioners, contributing to the advancement of novel approaches. Key aspects covered include the classification of deep learning applications, recent advancements in methods, general applications in the field, innovations, challenges, and limitations associated with learning-based navigation systems. This review also explores current research trends and future directions in the field. This extensive overview, initiated in 2020, provides a valuable resource for researchers of all levels, from seasoned experts to newcomers. Its main purpose is to streamline the process of identifying , evaluating, and interpreting relevant research, ultimately contributing to the progress and development of autonomous navigation technologies.
