Vehicle Navigation Applications Research Articles

GNSS/INS integrated navigation algorithm framework based on cascade vector tracking algorithm for USV

The safety of Unmanned Surface Vehicle during the autonomous phase of navigation relies heavily on the onboard navigation equipment. However, in the face of changing and diverse navigation environments, global satellite navigation systems utilizing radio communication often face signal occlusions. This significantly reduces the positioning accuracy of navigation systems. To ensure the navigation accuracy of vehicles in various harsh environments, a Global Navigation Satellite Systems and Inertial Navigation System (GNSS/INS) integrated navigation system structure based on cascaded vector-tracking loop is proposed in this paper. The integrated navigation scheme not only improved the positioning accuracy of the navigation system but also effectively improved the robustness of the system. Compared with the traditional scalar-tracking loop and vector-tracking loop methods, it exhibits better performance. The experimental results show that the proposed method can reduce the horizontal position positioning error by 73.7% compared with the traditional vector-tracking loop method in the case of signal occlusion. Simultaneously, it can continuously output reliable and high-precision positioning information in the case of serious signal occlusion. The integrated navigation scheme proposed in this paper can be used as a research direction for the development of Unmanned Surface Vehicle navigation applications and provides solutions for the development of related navigation systems.

A Hybrid RISS/GNSS Method During GNSS Outage in the Land Vehicle Navigation System

The integrated navigation system of an inertial navigation system (INS) and a global navigation satellite system (GNSS) is a standard solution in land vehicle navigation applications. Considering a low-cost solution, the reduced inertial sensor system (RISS) is adopted in place of INS to provide better navigation performance for land vehicles with fewer inertial sensors and lower computations. However, low-cost sensors can quickly deteriorate the navigation solution during GNSS outage. Hence, a novel RISS /GNSS method with the assistance of the long short-term memory (LSTM) neural network (NN), which has the ability of adaptive memorizing, is proposed to bridge GNSS outage by means of data fusion. In addition, zero-velocity detection is applied to advance the navigation performance provided by the LSTM algorithm during GNSS outage. We examined the performance of this method by using real road test experiments in a land vehicle equipped with GNSS receivers and inertial sensors in addition to a high-end GNSS /INS to provide the reference solution. During 300-s GNSS outage, the experimental results illustrate that this hybrid method based on the LSTM algorithm can enhance the navigation accuracy by 50% when compared with the standalone RISS algorithm, and provide 30% improvements in comparison with the nonlinear autoregressive with exogenous input (NARX) algorithm.

Improved decentralized GNSS/SINS/odometer fusion system for land vehicle navigation applications

Due to low cost and complementary performance advantages, global navigation satellite system (GNSS)/ strapdown inertial navigation system (SINS) integrated systems have established themselves in certain areas of land vehicle navigation. However, this integrated system cannot maintain reliable positioning solutions in challenging environments due to the inherent fragility of GNSS signals and time accumulated errors of a stand-alone SINS. To address this challenge, a multi-source information fusion system based on the decentralized system architecture and sequential Kalman filter for a land vehicle is proposed, which can fuse information from an odometer and motion aided constraints selectively and adaptively in different driving environments. Moreover, a comprehensive calibration and compensation strategy is designed to enhance the information fusion. On the one hand, a real-time calibration algorithm is designed to estimate the time-varying odometer scale factor and the misalignment between the inertial measurement unit (IMU) and vehicle body frame when GNSS signals are available. On the other hand, the forward velocity error caused by the lever arm, and the non-zero lateral velocity generated by the turning maneuver are compensated by the introduced velocity compensation method. A real car experiment in urban areas is carried out to illustrate the effectiveness of the proposed system. It shows that the proposed decentralized GNSS/SINS/odometer fusion system can maintain an average horizontal positioning root mean square error (RMSE) of 1-meter level when GNSS signals are cut off about 1-2 min. In addition, compared with the traditional centralized fusion structure, the proposed decentralized fusion structure can mitigate the horizontal positioning RMSE of the whole trajectory from 2.95 m to 0.59 m, which verifies that it can obtain better performance for the application of low-cost sensors in complex GNSS environments.

An Improved Adaptive Kalman Filter for a Single Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation Module

Aiming at the GNSS receiver vulnerability in challenging urban environments and low power consumption of integrated navigation systems, an improved robust adaptive Kalman filter (IRAKF) algorithm with real-time performance and low computation complexity for single-frequency GNSS/MEMS-IMU/odometer integrated navigation module is proposed. The algorithm obtains the scale factor by the prediction residual, and uses it to adjust the artificially set covariance matrix of the observation vector under different GNSS solution states, so that the covariance matrix of the observation vector changes continuously with the complex scene. Then, the adaptive factor is calculated by the Mahalanobis distance to inflate the state prediction covariance matrix. In addition, the one-step prediction Kalman filter is introduced to reduce the computational complexity of the algorithm. The performance of the algorithm is verified by vehicle experiments in the challenging urban environments. Experiments show that the algorithm can effectively weaken the effects of abnormal model deviations and outliers in the measurements and improve the positioning accuracy of real-time integrated navigation. It can meet the requirements of low power consumption real-time vehicle navigation applications in the complex urban environment.

ORB-SLAM accelerated on heterogeneous parallel architectures

SLAM algorithm permits the robot to cartography the desired environment while positioning it in space. It is a more efficient system and more accredited by autonomous vehicle navigation and robotic application in the ongoing research. Except it did not adopt any complete end-to-end hardware implementation yet. Our work aims to a hardware/software optimization of an expensive computational time functional block of monocular ORB-SLAM2. Through this, we attempt to implement the proposed optimization in FPGA-based heterogeneous embedded architecture that shows attractive results. Toward this, we adopt a comparative study with other heterogeneous architecture including powerful embedded GPGPU (NVIDIA Tegra TX1) and high-end GPU (NVIDIA GeForce 920MX). The implementation is achieved using high-level synthesis-based OpenCL for FPGA and CUDA for NVIDIA targeted boards.

Low-Cost Real-Time PPP/INS Integration for Automated Land Vehicles.

The last decade has witnessed a growing demand for precise positioning in many applications including car navigation. Navigating automated land vehicles requires at least sub-meter level positioning accuracy with the lowest possible cost. The Global Navigation Satellite System (GNSS) Single-Frequency Precise Point Positioning (SF-PPP) is capable of achieving sub-meter level accuracy in benign GNSS conditions using low-cost GNSS receivers. However, SF-PPP alone cannot be employed for land vehicles due to frequent signal degradation and blockage. In this paper, real-time SF-PPP is integrated with a low-cost consumer-grade Inertial Navigation System (INS) to provide a continuous and precise navigation solution. The PPP accuracy and the applied estimation algorithm contributed to reducing the effects of INS errors. The system was evaluated through two road tests which included open-sky, suburban, momentary outages, and complete GNSS outage conditions. The results showed that the developed PPP/INS system maintained horizontal sub-meter Root Mean Square (RMS) accuracy in open-sky and suburban environments. Moreover, the PPP/INS system could provide a continuous real-time positioning solution within the lane the vehicle is moving in. This lane-level accuracy was preserved even when passing under bridges and overpasses on the road. The developed PPP/INS system is expected to benefit low-cost precise land vehicle navigation applications including level 2 of vehicle automation which comprises services such as lane departure warning and lane-keeping assistance.

Stepwise fusion algorithm with dual correction for multi-sensor navigation

In high-altitude, long-endurance unmanned aerial vehicles, a celestial attitude determination system is used to enhance the inertial navigation system (INS)/global positioning system (GPS) to achieve the required attitude performance. The traditional federal filter is not applicable for INS/GPS/celestial attitude determination system information fusion because it does not consider the mutually coupled relationship between the horizontal reference error in the celestial attitude determination system and the navigation error; this limitation results in reduced navigation accuracy. This article proposes a novel stepwise fusion algorithm with dual correction for multi-sensor navigation. Considering the horizontal reference error, the celestial attitude determination system measurement model is constructed and the issues involved in applying the federal filter are discussed. Then, preliminary error estimation and horizontal reference compensation are added to the navigation architecture. In addition, a sequential update strategy is derived to estimate the attitude error with the compensated celestial attitude determination system based on the preliminary estimation. A stepwise correction filtering algorithm with interactive preliminary and sequential updates that can effectively fuse celestial attitude determination system measurements with the INS/GPS is constructed. High-altitude, long-endurance unmanned aerial vehicle navigation in a remote sensing task is simulated to verify the performance of the proposed method. The simulation results demonstrate that the horizontal reference error is effectively compensated, and the attitude accuracy is significantly improved after stepwise error estimation and correction. The proposed method also provides a novel multi-sensor integrated navigation architecture with mutually coupled errors; this architecture is beneficial in unmanned aerial vehicle navigation applications.

Improved visual SLAM: a novel approach to mapping and localization using visual landmarks in consecutive frames

Pathfinding is becoming more and more common in autonomous vehicle navigation, robot localization, and other computer vision applications. In this paper, a novel approach to mapping and localization is presented that extracts visual landmarks from a robot dataset acquired by a Kinect sensor. The visual landmarks are detected and recognized using the improved scale-invariant feature transform (I-SIFT) method. The methodology is based on detecting stable and invariant landmarks in consecutive (red-green-blue depth) RGB-D frames of the robot dataset. These landmarks are then used to determine the robot path, and a map is constructed by using the visual landmarks. A number of experiments were performed on various datasets in an indoor environment. The proposed method performs efficient landmark detection in various environments, which includes changes in rotation and illumination. The experimental results show that the proposed method can solve the simultaneous localization and mapping (SLAM) problem using stable visual landmarks, but with less computation time.

A Novel Dynamic Physical Storage Model for Vehicle Navigation Maps

The physical storage model is one of the key technologies for vehicle navigation maps used in a navigation system. However, the performance of most traditional storage models is limited in dynamic navigation due to the static storage format they use. In this paper, we proposed a new physical storage model, China Navigation Data Format (CNDF), which helped access and update the navigation data. The CNDF model used the reach-based hierarchy method to build a road hierarchal network, which enhanced the efficiency of data compression. It also adopted the Linear Link Coding method, in which the start position was combined with the end position as the identification code for multi-level links, and each link traced up-level links consistently without recording the array of identifications. The navigation map of East China (including Beijing, Tianjin, Shandong, Hebei, and Jiangsu) at 1:10,000, generated using the CNDF model, and the real time traffic information in Beijing were combined to test the performance of a navigation system using an embedded navigation device. Results showed that it cost less than 1 second each time to refresh the navigation map, and the accuracy of the hierarchal shortest-path algorithm was 99.9%. Our work implied that the CNDF model is efficient in vehicle navigation applications.

Improving MEMS-IMU/GPS integrated systems for land vehicle navigation applications

The cost of inertial navigation systems (INS) has decreased significantly during recent years using micro-electro-mechanical system technology in production of inertial measurement units (IMUs). However, these IMUs do not provide the accuracy and stability of their classical mechanical counterparts which limit their applications. Hence, the error control of such systems is of the great importance which is achievable using external information via an appropriate fusion algorithm. Traditionally, this external information can be derived from global positioning system (GPS). But it is well known that GPS data availability and accuracy are vulnerable to signal-degrading circumstances and satellite visibility. We introduce a standalone attitude and heading reference system (AHRS) algorithm which employs the IMU and magnetometers data in an averaging manner. The averaging method is different from a simple smoothing procedure, since it takes the rotations of the platform (during the averaging interval) into account. The proposed AHRS solution is further used to provide additional attitude updates with adaptive noise variances for the integrated INS/GPS system during GPS outages via a refined loosely coupled filtering procedure, making the error growth well restrained. Functionality of the algorithm has been assessed via a field test. The results indicate that the proposed procedure outperforms the traditional integration scheme in different situations, while the latter almost loses track of the movements of the vehicle after 60-second GPS outages.

A novel image fusion framework for night-vision navigation and surveillance

The night-vision image fusion plays a critical role in detecting targets and obstructions in low light or total darkness, which has great importance for pedestrian recognition, vehicle navigation, surveillance and monitoring applications. The central idea is to fuse low-light visible and infrared imagery into a single output. In this paper, we describe a new fusion framework for spatially registered visual and infrared images. The proposed framework utilizes the properties of fractal dimension and phase congruency in the non-subsampled contourlet transform (NSCT) domain. The proposed framework applies multiscale NSCT on visual and IR images to get low- and high-frequency bands. The varied frequency bands of the transformed images are then fused while exploiting their characteristics. Finally, the inverse NSCT is performed to get the fused image. The performance of the proposed framework is validated by extensive experiments on different scene imaginary, where the definite advantages are demonstrated subjectively and objectively.

GPS/INS/Odometer Integrated System Using Fuzzy Neural Network for Land Vehicle Navigation Applications

The integration of Global Positioning Systems (GPS) with Inertial Navigation Systems (INS) has been very actively studied and widely applied for many years. Some sensors and artificial intelligence methods have been applied to handle GPS outages in GPS/INS integrated navigation. However, the integrated system using the above method still results in seriously degraded navigation solutions over long GPS outages. To deal with the problem, this paper presents a GPS/INS/odometer integrated system using a fuzzy neural network (FNN) for land vehicle navigation applications. Provided that the measurement type of GPS and odometer is the same, the topology of a FNN used in a GPS/INS/odometer integrated system is constructed. The information from GPS, odometer and IMU is input into a FNN system for network training during signal availability, while the FNN model receives the observations from IMU and odometer to generate odometer velocity correction to enhance resolution accuracy over long GPS outages. An actual experiment was performed to validate the new algorithm. The results indicate that the proposed method can improve the position, velocity and attitude accuracy of the integrated system, especially the position parameters, over long GPS outages.

A Practical Method for Implementing an Attitude and Heading Reference System

This paper describes a practical and reliable algorithm for implementing an Attitude and Heading Reference System (AHRS). This kind of system is essential for real time vehicle navigation, guidance and control applications. When low cost sensors are used, efficient and robust algorithms are required for performance to be acceptable. The proposed method is based on an Extended Kalman Filter (EKF) in a direct configuration. In this case, the filter is explicitly derived from both the kinematic and error models. The selection of this kind of EKF configuration can help in ensuring a tight integration of the method for its use in filter-based localization and mapping systems in autonomous vehicles. Experiments with real data show that the proposed method is able to maintain an accurate and drift-free attitude and heading estimation. An additional result is to show that there is no ostensible reason for preferring that the filter have an indirect configuration over a direct configuration for implementing an AHRS system.

Dempster Shafer neural network algorithm for land vehicle navigation application

A Global Positioning System (GPS)–aided Inertial Navigation System (INS) provides a continuous navigation solution with reduced uncertainty and ambiguity. Bayesian approaches like Extended Kalman filter or Particle filter are generally developed for fusing the GPS and INS data. However, these techniques require prior distribution (representing the degree of belief) to be accurately defined for all incorporated parameters−whether known or unknown. If no previous knowledge is obtainable, equal probabilities are assigned to all events, which is questionable. To overcome these limitations, Dempster Shafer (DS) evidence theory is implemented in this paper. In order to effectively fuse GPS and INS data for land vehicle navigation application, we propose an efficient Dempster Shafer Neural Network (DSNN) algorithm by integrating the Dempster Shafer theory and the artificial neural network. Our field test results clearly indicate that the proposed DSNN algorithm effectively compensated and reduced positional inaccuracies during no GPS outage and GPS outage conditions for low cost inertial sensors.

Performance Analysis of Medium Accuracy FOG-Based IMU Integrated with Optical Odometer for Land Vehicle Navigation

The aim of this work is to research the feasibility of using optical odometer as the aided sensor for accuracy improvement of medium accuracy (FOG)-based IMU for land vehicle navigation. Usually GNSS is integrated with low cost SINS (strapdown inertial navigation systems) for land vehicle navigation but GNSS is not always reliable in land vehicle applications. The focus is on analysis of position and velocity accuracy of SINS/Odometer integration using close loop kalman filter. Integrated navigation algorithm for vehicle states estimation and correction have been designed and implemented. The measurement error model for odometer in navigation frame is developed for Kalman filter implementation. As the prior knowledge of measurement noise which represents the stochastic properties of odometer is not exactly known, so an adaptive Kalman filter (AKF) is also proposed for online estimation of the measurement noise matrix in order to improve the accuracy. For the performance analysis of the designed system, field test is carried out and results show that the accuracy of the medium accuracy fiber optics gyro (FOG)-based SINS is improved and the systems is capable for land vehicle navigation application

Extraction of Visual Landmarks Using Improved Feature Matching Technique for Stereo Vision Applications

Stereo vision is becoming more and more common in three-dimensional visualization, autonomous vehicle navigation, path finding, object recognition, and other computer vision applications. In this paper, we present an approach for extracting visual landmarks from images acquired by a stereoscopic camera. The scale invariant feature transform (SIFT) technique with self-organizing map is used to detect and recognize visual landmarks. Our methodology is based on winner calculation technique, and the main idea is to keep in the database only distinctive features or landmarks in order to minimize detection time. We will demonstrate that this methodology is more efficient than ordinary SIFT algorithm or speeded up robust features (SURF) matching technique. Improved feature group matching with computation time better than SIFT and the SURF has been observed in the experiments with a variety of stereo image pairs.

An Improved GPS/RFID Integration Method Based on Sequential Iterated Reduced Sigma Point Kalman Filter

In this paper, an improved GPS/RFID integration method based on Sequential Iterated Reduced Sigma Point Kalman Filter (SIRSPKF) is proposed for vehicle navigation applications. It is applied to improve the accuracy, reliability and availability of satellite positioning in the areas where the satellite visibility is limited. An RFID system is employed to assist the GPS system in achieving high accuracy positioning. Further, to reduce the measurement noise and decrease the computational complexity caused by the integrated GPS/RFID, SIRSPKF is investigated as the dominant filter for the proposed integration. Performances and computational complexities of different integration scenarios with different filters are compared in this paper. A field experiment shows that both accuracy and availability of positioning can be improved significantly by this low-cost GPS/RFID integration method with the reduced computational load

Optimal Synthesis of the Asymmetric Sinistral/Dextral Markov–Dubins Problem

We consider a variation of the classical Markov–Dubins problem dealing with curvature-constrained, shortest paths in the plane with prescribed initial and terminal positions and tangents, when the lower and upper bounds of the curvature of the path are not necessarily equal. The motivation for this problem stems from vehicle navigation applications, when a vehicle may be biased in taking turns at a particular direction due to hardware failures or environmental conditions. After formulating the shortest path problem as a minimum-time problem, a family of extremals, which is sufficient for optimality, is characterized, and subsequently the complete analytic solution of the optimal synthesis problem is presented. In addition, the synthesis problem, when the terminal tangent is free, is also considered, leading to the characterization of the set of points that can be reached in the plane by curves satisfying asymmetric curvature constraints.

A Hybrid Particle Approach for GNSS Applications With Partial GPS Outages

To provide an accurate positioning, the land vehicle navigation applications are based on global positioning system (GPS). The addition of a digital road map allows locating the vehicle continuously and helps the driver to get the best path. These systems are usually enhanced with dead reckoning sensors due to GPS outages in urban areas in particular. For instance, the odometer sensors can be used to correct the vehicle location in this case. We present here a global estimation method of solving the fusion problem of the GPS, odometer, and digital road map measurements in the presence of GPS outages. It relies on a hybrid filter that takes advantage of the combination of a Kalman filter, which computes the linear part of the state equations and a particle filter to provide an optimal resolution scheme. When GPS fails, the filter fuses all available pseudorange measures to improve the vehicle positioning. In the case of an urban transport scenario, the results show that the number of particles is significantly reduced to achieve the same performance of a single particle filter in terms of accuracy. Moreover, software solutions can be developed for real-time applications.

GPS/INS integration utilizing dynamic neural networks for vehicular navigation

Recently, methods based on Artificial Intelligence (AI) have been suggested to provide reliable positioning information for different land vehicle navigation applications integrating the Global Positioning System (GPS) with the Inertial Navigation System (INS). All existing AI-based methods are based on relating the INS error to the corresponding INS output at certain time instants and do not consider the dependence of the error on the past values of INS. This study, therefore, suggests the use of Input-Delayed Neural Networks (IDNN) to model both the INS position and velocity errors based on current and some past samples of INS position and velocity, respectively. This results in a more reliable positioning solution during long GPS outages. The proposed method is evaluated using road test data of different trajectories while both navigational and tactical grade INS are mounted inside land vehicles and integrated with GPS receivers. The performance of the IDNN – based model is also compared to both conventional (based mainly on Kalman filtering) and recently published AI – based techniques. The results showed significant improvement in positioning accuracy especially for cases of tactical grade INS and long GPS outages.