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This paper proposes to use a newly-derived transformed inertial navigation system (INS) mechanization to fuse INS with other complementary navigation systems. Through formulating the attitude, velocity and position as one group state of group of double direct spatial isometries SE2(3), the transformed INS mechanization has proven to be group affine, which means that the corresponding vector error state model will be trajectory-independent. In order to make use of the transformed INS mechanization in inertial based integration, both the right and left vector error state models are derived. The INS/GPS and INS/Odometer integration are investigated as two representatives of inertial based integration. Some application aspects of the derived error state models in the two applications are presented, which include how to select the error state model, initialization of the SE2(3) based error state covariance and feedback correction corresponding to the error state definitions. Extensive Monte Carlo simulations and land vehicle experiments are conducted to evaluate the performance of the derived error state models. It is shown that the most striking superiority of using the derived error state models is their ability to handle the large initial attitude misalignments, which is just the result of log-linearity property of the derived error state models. Therefore, the derived error state models can be used in the so-called attitude alignment for the two applications. Moreover, the derived right error state-space model is also very preferred for long-endurance INS/Odometer integration due to the filtering consistency caused by its less dependence on the global state estimate.

The Global Positioning System (GPS) is not an effective solution for pedestrian indoor navigation using embedded devices because of poor signal penetration and high power requirements. Indoor positioning based purely on commercial-grade inertial measurement units (IMUs) may provide a good alternative but their significant noise and random bias must be eliminated. Although Kalman filters and pedestrian dead reckoning helped reduce the errors using IMUs, these methods cannot prevent the divergence of estimated position error. Deep learning was proposed for accurate position estimation with IMUs. Despite the progress, robust position estimation in mobile embedded devices using deep learning has not been established yet because of memory requirements, latency and inaccurate position estimation especially for stationary pedestrians. In this study, we present an efficient embedded deep learning approach for robust, real-time and accurate pedestrian position estimation using commercial Android smartphone IMUs. We first extended a publicly available deep inertial navigation dataset (OxIOD) with stationary data to enhance the positioning accuracy for both steady state and motion. Next, we trained and tested a deep learning architecture that yields a higher positioning accuracy with 50% lower network latency and 31% lower network size compared with earlier deep positioning networks such as IONet. Our real-time tests of the model in an Android smartphone indicated that the extension for the dataset reduces the position shift when the smartphone is stationary. Since our embedded deep learning solution simultaneously decreases the positioning error, latency and memory requirements, the solution paves the way for numerous practical indoor navigation applications.

The field of automatic driving has always been a research hotspot. Based on inertial and odometer, dead reckoning is used to navigate for L4 and above level autonomous vehicles with less human intervention when GPS is blocked. The navigation accuracy is mainly determined by the alignment error and the drift accumulated over time. In this paper, we proposed a novel method to achieve initial alignment of inertial navigation for L4 and above level autonomous vehicles. The alignment is accomplished with a MEMS inertial measurement unit (MIMU) and single antenna global positioning system real-time kinematic (GPS-RTK). By matching the trajectory obtained from MIMU dead reckoning with the trajectory obtained from RTK, the initial heading angle is obtained, and the heading angle accuracy is improved through multiple iterations. In the case of RTK precision decreasing, the MIMU can still achieve lane-level navigation after the initial alignment. Because the alignment is accomplished in motion, there is no need for additional static alignment time before the vehicle starts. The field experiment results show that the method could achieve fast initial alignment in a few seconds in moving state with an alignment accuracy of 0.05°.

Radar mounted on a moving vehicle returns time-varying detections corresponding to unstable scattering points, unlike optical sensors, which produce relatively stable detections. We present an efficient extended Kalman filter-based simultaneous localization and mapping (EKF-SLAM) algorithm for radar, utilizing new techniques of clustering and sifting the time-varying detections. Velocity bias and yaw rate bias, which are inherent in any odometer are also estimated and compensated using the same EKF. For theoretical performance evaluation, the posterior Cramer–Rao bound (PCRB) for SLAM with odometer bias estimation is derived and compared with the root-mean-squared errors (RMSEs), with and without bias estimation. The simulation results show that the RMSE for SLAM with bias estimation is the closest to the PCRB. The proposed algorithm is also verified experimentally with field data. The comparison with the ground truth trajectory obtained from a differential global positioning system shows that the proposed algorithm yields an accurate trajectory estimate, even under large odometer bias. Also, the real-time capability of the proposed algorithm is verified.

The high-precision positioning of the shearer is the key technology to realize the automation of longwall mining. Since mine is a Global Position System (GPS)-denied environment, highly autonomous Inertial Navigation System (INS)/odometer integrated navigation has been widely used. At present, the shearer positioning method based on INS/odometer has been challenging to meet the requirements of long-time and high-precision mining. Aiming at the high-precision navigation in the complex mining environment, this paper constructs a comprehensive rail kinematics model of the shearer that does not rely on external sensors. By analyzing the kinematic characteristics of the shearer and the scraper conveyor during the longwall mining process, a method of information fusion and navigation system fault diagnosis based on the assistance of the shearer rail kinematics model was proposed. According to the working principle of the shearer rails and hydraulic supports, the characteristics of the trajectory deviation caused by the sensor fault of the hydraulic support are analyzed. Combined with the engineering requirements of shearer mining, the model fault identification was carried out by the fading probability ratio detection algorithm. The simulation results show that the proposed algorithm effectively improves the positioning of shearer accuracy in multiple cutting cycles. At the same time, it avoids the influence of the rail deviation caused by the rail kinematics model fault on the positioning of the shearer.

Global Navigation Satellite Systems (GNSS) integrated with multiple sensors have been widely applied in many Intelligent Transport Systems (ITS). Intelligent vehicles, increasingly a key component of the future transport system, require high-performance positioning, navigation and timing (PNT) technologies. This cannot be achieved by current 2-dimnsional systems. This is because accurate comprehensive state information (time, position, derivatives and attitude) is required to estimate amongst others, the vehicle’s power usage as well as to enable precise environmental mapping based on accurate state of the relevant sensors and path planning including in complex multi-level intersections. Because of the multipath effects and signal interruption in urban environments, comprehensive vehicle state estimation is not always available at the required level of performance. To address this issue, we propose an effective method to integrate the Inertial Measurement Unit (IMU), Global Positioning System (GPS) and monocular Visual Odometry (VO) for urban vehicle navigation. A robust Extended Kalman Filter (EKF) based two-step integration algorithm is developed with a non-holonomic constraint (NHC). In particular, the NHC is not only applied on the offline VO error modelling process, but also on the online sensor fusion process to improve the 3D vehicle state estimation. The proposed IMU/GPS/VO integration scheme is tested with various sensor levels in different urban environments. The results show that the proposed IMU/GPS/VO fusion algorithm could deliver a 3D RMSE of 3.285m, which outperforms the other conventional candidate fusion schemes in the noisy GNSS urban areas. The further test in the urban with outages has demonstrated that the proposed algorithm delivers an overall 3D RMSE of 1.290m, 0.073m/s and 0.486 degrees, in terms of positioning, velocity and attitude, respectively. It is also demonstrated that the proposed low-cost VO integration with IMU/GPS could achieve similar performance with the high-cost odometer based integration in deep urban areas but with advantages of higher flexibility and lower cost.

The technique proposed in this research demonstrates a real time nonlinear data fusion solution based on extremely low-cost and grade inertial sensors for land vehicle navigation. Here, the utilized nonlinear multi-sensor data fusion (MSDF) is based on the combination between extremely low-cost micro electrical mechanical systems (MEMS) inertial, heading, pressure, and speed sensors in addition to satellite-based navigation system. The integrated navigation system fuses position and velocity states from the Global Positioning System (GPS), the velocity measurements from an odometer, heading angle observation from a magnetometer and navigation states from an inertial navigation system (INS). The implemented system performance is assessed through the post-processing of collected raw measurements and real time experimental work. The system that runs the real-time experiments is established on three connected platforms, two of them are based on a 32-bit ARMTM core and the third one is based 16-bit AVR ATMEL microcontroller. This microcontroller is connected to an on-board diagnostics (OBD) shield to collect the vehicle speed measurements. The raw data obtained from the integrated sensors is saved and post processed in MATLAB®. In normal conditions, the estimated position errors are reduced through the usage of INS/GPS integration with heading observation angle from a magnetometer. In GPS-denied environments, the integrated system uses the observations from INS, magnetometer in addition to the velocity from odometer to ensure a continuous and accurate navigation solution. A complementary filter (CF) is implemented to estimate and improve the pitch and roll angles calculations. In addition to that, an unscented Kalman filter (UKF) is used cascaded with the designed CF to complete the designed sensors fusion algorithm. Experimental results show that the designed MSDF can achieve a good level of accuracy and a continuous localization solution of a land vehicle in different GPS availability cases and can be implemented on the available in the market processors to be run in real time.

SUMMARYIn mobile robot localization with multiple sensors, myriad problems arise as a result of inadequacies associated with each of the individual sensors. In such cases, methodologies built upon the concept of multisensor fusion are well-known to provide optimal solutions and overcome issues such as sensor nonlinearities and uncertainties. Artificial neural networks and fuzzy logic (FL) approaches can effectively model sensors with unknown nonlinearities and uncertainties. In this article, a robust approach for localization (positioning) of a mobile robot in indoor as well as outdoor environments is proposed. The neural network is utilized as a pseudo-sensor that models the global positioning system (GPS) and is used to predict the robot’s position in case of GPS signal loss in indoor environments. The data from proprioceptive sensors such as inertial sensors and GPS are fused using the Kalman and the complementary filter-based fusion schemes in the outdoor case. To eliminate the position inaccuracies due to wheel slippage, an expert FL system (FLS) is implemented and cascaded with the sensor fusion module. The proposed technique is tested both in simulation and in real scenarios of robot movements. The simulations and results from the experimental platform validate the efficacy of the proposed algorithm.

Although the accuracy of MEMS gyroscopes has been extremely improved, in some aspects, such as stability of bias, they still suffer from some big error sources, like run-to-run bias, which determines the sensor price but is not negligible even inexpensive sensors. Due to the fact that run-to-run bias is a kind of stochastic parameter, it has to be measured by utilizing online methods. Utilizing a novel, fast and efficient vision-based rotation estimation algorithm for ground vehicles, we have developed a visual gyroscope that is used in our sensor fusion system, in order to estimate run-to-run bias of the MEMS gyroscope, accurately. Comparing with similar approaches that use GPS, odometer, accelerometer or magnetometer, the most important advantage of the proposed vision-based sensor-fusion framework is its accuracy in accelerated motions. Moreover, it can be used at environments that have a magnetic field, such as urban environments, without depending on external signals. We have evaluated the efficiency of the proposed system using real datasets, recorded from a car moving in urban areas. According to our experimental results, the proposed algorithm is capable of estimating bias of gyroscope after a convergence time of about 6 seconds and improving the accuracy of the MEMS gyroscope, which provides the possibility of using cheaper sensors for high-accuracy demands.

Global Navigation Satellite System Real-Time Kinematic (GNSS-RTK) technology is widely used in vehicle navigation, but in complex environments such as urban high-rise street, wooded street, overpass, and tunnel, satellite signals are prone to attenuation or even unavailability. It brings great challenges to the continuous high-precision navigation. For this reason, a tightly coupled (TC) integration algorithm for GPS (Global Positioning System)/BDS (BeiDou Navigation Satellite System)/MEMS-INS (Micro-Electro-Mechanical System-Inertial Navigation System)/Odometer (GCIO) is proposed for vehicle navigation in complex urban environments. The accuracy improvement and ambiguity resolution (AR) performance are analysed in this research. First of all, the INS positioning error is constrained by fusion GPS/BDS (GC) and odometer; then, the predicted position information is used to aid GPS/BDS ambiguity resolution. In GNSS-denied environments, the odometer/INS integration is still carried out for continuous navigation. Real-time experiments are carried out in urban degraded and denied environments to validate the performance of the integrated system. In high-rise streets, the ambiguity fixing success rate of GCIO mode is 13.57% higher than that of GC mode. In the wooded street environment, the success rate has increased particularly significantly, by about 55 percent. The positioning accuracy analysis for open environment, high-rise street, wooded street, overpass, and tunnel is conducted. The experimental results show that in the above environment, the order of 0.1 m positioning accuracy can be achieved in the case of satellite outage for 1 minute, which can meet the positioning needs in most scenarios.

This work deals with the development of an adaptive multisensor data fusion technique for the accurate estimation of the trains position and velocity. The proposed technique will work with the Train Collision Avoidance System (TCAS) used in Indian railways during Global Positioning System (GPS) outages. The determination of accurate position of trains is a challenging task for the TCAS during GPS outages. The accuracy of the proposed Volterra Recursive Least Square (VRLS) based adaptive multisensor data fusion technique is evaluated by generating two kinematic profiles for a passenger train running between Silchar–Lumding broad gauge route in Indian railways. The effect of accelerometer bias is also considered during the analysis. It is observed that the developed technique can provide a better estimate of the position and velocity for the TCAS especially during GPS outages and without using any additional railway infrastructure. The simulation results indicate that the proposed technique is superior to the earlier works in terms of achieving better positional accuracy in presence of accelerometer bias.

Accurate geographic location of a pipeline is important information for pipeline maintenance and leak detection. Usually, the geographic location of a pipeline on the ground can be measured directly by global positioning system (GPS) technology, but it is much difficult to determine the geographic location of an underground pipeline. In this paper, a new technique based on the developed pipeline crawler is proposed for mapping of underground small-diameter gas pipeline topology. The pipeline crawler is equipped with a micro electro mechanical system (MEMS) based inertial measurement unit (IMU) and odometers. The kinematic model and the measurement model of the pipeline crawler are established based on the IMU and odometers. Mapping of pipeline topology is completed by the sensor fusion algorithm which is proposed to reconstruct crawler path using sensor data and pipeline features. The experiment is given to illustrate the advantage of the new pipeline mapping technique.

Most UAVs rely on GPS for localization in an outdoor environment. However, in GPS-denied environment, other sources of localization are required for UAVs to conduct feedback control and navigation. LiDAR has been used for indoor localization, but the sampling rate is usually too low for feedback control of UAVs. To compensate this drawback, IMU sensors are usually fused to generate high-frequency odometry, with only few extra computation resources. To achieve this goal, a real-time LiDAR inertial odometer system (RTLIO) is developed in this work to generate high-precision and high-frequency odometry for the feedback control of UAVs in an indoor environment, and this is achieved by solving cost functions that consist of the LiDAR and IMU residuals. Compared to the traditional LIO approach, the initialization process of the developed RTLIO can be achieved, even when the device is stationary. To further reduce the accumulated pose errors, loop closure and pose-graph optimization are also developed in RTLIO. To demonstrate the efficacy of the developed RTLIO, experiments with long-range trajectory are conducted, and the results indicate that the RTLIO can outperform LIO with a smaller drift. Experiments with odometry benchmark dataset (i.e., KITTI) are also conducted to compare the performance with other methods, and the results show that the RTLIO can outperform ALOAM and LOAM in terms of exhibiting a smaller time delay and greater position accuracy.

Purpose This paper aims to propose a series of approaches to solve the problem of the mobile robot motion control and autonomous navigation in large-scale outdoor GPS-denied environments. Design/methodology/approach Based on the model of mobile robot with two driving wheels, a controller is designed and tested in obstacle-cluttered scenes in this paper. By using the priori “topology-geometry” map constructed based on the odometer data and the online matching algorithm of 3D-laser scanning points, a novel approach of outdoor localization with 3D-laser scanner is proposed to solve the problem of poor localization accuracy in GPS-denied environments. A path planning strategy based on geometric feature analysis and priority evaluation algorithm is also adopted to ensure the safety and reliability of mobile robot’s autonomous navigation and control. Findings A series of experiments are conducted with a self-designed mobile robot platform in large-scale outdoor environments, and the experimental results show the validity and effectiveness of the proposed approach. Originality/value The problem of motion control for a differential drive mobile robot is investigated in this paper first. At the same time, a novel approach of outdoor localization with 3D-laser scanner is proposed to solve the problem of poor localization accuracy in GPS-denied environments. A path planning strategy based on geometric feature analysis and priority evaluation algorithm is also adopted to ensure the safety and reliability of mobile robot’s autonomous navigation and control.

Traditional vehicle localization methods use information from a GPS, inertial navigation, and an odometer, but a GPS is often limited by availability in urban areas for its sensitivity to terrain and interference. Aiming at the above problems, this paper presents a method of intersection localization using a traffic lights’ map. In particular, traffic lights with significant visual characteristics in the urban environment are used as landmarks. The localization accuracy of the autonomous vehicle at intersections is improved by combining the location information of traffic lights provided by a high-precision map. The whole scheme of the localization system is designed and the sensors used are determined. First, the coordinates of sensors are established, respectively, and the transformation and rotation are calibrated. Then, the state model and measurement model of the vehicle vision system are established. Combined with the location and height information of the traffic lights provided by the high-precision map, the extended Kalman filter is used to fuse the vision detection results of the traffic lights with the inertial measurement unit information. The experiments demonstrate that the method proposed in this paper improves the lateral localization accuracy and the accuracy of the vehicle’s yaw angle.

In-motion alignment of navigation-grade strapdown inertial navigation system (SINS) combined with odometer (OD) is one of the challenging issues for the land vehicle navigation, while most current in-motion alignment methods still rely on the measurements of the global positioning system or only aim at improving the attitude alignment, thus neglecting the position alignment. Thus, one of the main obstacles during the in-motion alignment for SINS/OD is to autonomously and simultaneously obtain the high-precision attitude and position within specified time. In this paper, an in-motion alignment scheme based on the backtracking scheme is presented to solve this problem. The proposed method consists of two steps. First, an improved optimization-based coarse alignment (IOBCA) is employed to obtain the attitude and position with reasonable precision, and the gravity deflexion error projection in inertial frame caused by the positioning error is diminished, which contributes to better alignment results for the subsequent process. Second, initialized with the alignment results provided by IOBCA, a backtracking-scheme-based fine alignment combined with the Kalman filter is investigated to make a further improvement in the precision of attitude and position, during which the known initial binding position and zero velocity have been utilized again. The car-mounted field experimental results illustrate that the proposed method can not only autonomously improve the precision of attitude within a short time but also simultaneously achieve the position with high precision.

In this paper, we propose a MEMS 3D DR/GPS integrated system which provides 3D attitude and position information for land vehicle application. Since low-cost MEMS-IMU provides data with large bias, it is hard to obtain an accurate position of the land vehicle, especially during GPS outages. To improve the performance of the MEMS-based navigation system, odometer-based 3D DR/GPS formulation is proposed. The proposed algorithm uses 1D speed information for obtaining 3D position and extended Kalman filter (EKF) to compensate for navigation errors. An error model for the EKF is developed, including the odometer scale factor, which may change over various environments and require an accurate value. By substituting a MEMS INS/GPS system to 3D DR/GPS system, the velocity and position errors of navigation solution are reduced. Since the proposed system estimates 3D attitude and position unlike conventional DR/GPS system, it suppresses navigation errors effectively when a land vehicle goes uphill and downhill even for the GPS outages. Besides, the adaptation rule for measurement noise covariance is applied to improve navigation performance. The proposed method is tested for a land vehicle in an urban area, including GPS outages. The experimental results show that the proposed MEMS 3D DR/GPS system provides a more stable navigation solution with fewer position errors.

Accuracy of dynamic precise point positioning (PPP) degrades significantly under users’ challenging environments because of the limited or unavailable GPS observations. To overcome such weakness, a tight-integration system of micro-electromechanical systems (MEMS) inertial measurement unit (IMU), odometer, and GPS ionospheric delay constrained PPP is investigated to provide users positions and attitudes with higher accuracy, reliability, and continuity under the weak GPS observability environments. In this approach, the data from different sensors are integrated in two joint extend Kalman filters (EKF). One is for PPP/INS tight-integration and the other one is for odometer aiding the solutions from INS or PPP/INS tight-integration modes. Here, the advantages of slow time-varying ionospheric delay, high-accuracy in short time of inertial navigation system (INS), and hardly-disturbed odometer are utilized effectively to enhance PPP solutions. The corresponding mathematical models are provided and are evaluated by a set of one-hour real land-borne test data collected from a MEMS IMU, an odometer, and a dual-frequency GPS receiver in suburbs of Wuhan city, China, and also a set of simulated weak satellites availability data which is to imitate the GPS availability under unban canyons. Results indicate that the fusion system can improve navigation accuracy of GPS or GPS/INS significantly. That are, more than fifty percentages position enhancements in north-east-down components, and about forty percentages attitude enhancements in roll-pitch-yaw direction under the weak GPS availability.

Strapdown inertial navigation system (SINS) based vehicle weapon system increases great significance in land navigation. In order to obtain considerable navigation performance, SINS based integrated navigation is required to implemented. However, the popular aided sensor such as Global-Positioning-System (GPS) is not regarded as autonomous sensor. The autonomous sensor such as odometer can be also damaged in practical applications. In this paper, we utilize non-holonomic constraints (NHC) to aid SINS without external sensors. The measurements of NHC include zero lateral velocity and zero vertical velocity in vehicle orthogonal reference frame. Then, the close-loop Kalman filter can be implemented for SINS/NHC integrated navigation. The simulation results show that the SINS/NHC integrated navigation can estimate all the error states with specific movements. Moreover, SINS/NHC can achieve considerable position navigation performance. Therefore, the proposed method can realize autonomous land navigation without external sensors.

Considering that GPS based quadrotor can not fully realize positioning and navigation in an unknown environment and further environmental exploration. In this paper, we present our design for a system on a quadrotor, which is equipped with a 3D Lidar and camera, to explore and reconstruct the unknown scenes in real-time. Firstly, real-time positioning and sparse mapping are established to obtain quadrotor position information from Lidar points. Among them, for the successfully established sparse map, local map optimization is performed to complete the quadrotor pose estimation. Besides, through the fusion of Lidar and camera, the coloured point cloud information is obtained. Further, 3D surface reconstruction and updation are obtained based on TSDF by using real-time lidar odometer and colour point cloud information. Finally, the experiments verify that the platform can be used for SLAM and 3D reconstruction of unknown indoor and outdoor environments with better performance.

Location estimation for ground and air vehicles is mostly realized by fusion of global positioning system (gps / gnss) and data from sensors such as inertial measurement unit, barometer, wheel odometer, etc. However, in case GPS is not available, location estimation is an open problem. Considering the various advantages and disadvantages of all these sensors that can be used for position estimation, it is noticed that there is no single and absolute solution to the problem of threedimensional, 6 degrees-of-freedom localization. Especially in GPS-denied environments, precise localization methods for ground and air vehicles are still an important research area. While these periods of studies on autonomous vehicles gained speed, the localization problem with camera and lidar has been an alternative research topic which has strengthened its place in the literature. In this study; a new solution is proposed that addresses the problem of 6-DOF localization using a single camera and lidar where gps signal is not available.

GPS precise point positioning (PPP) is increasingly being used in many precise positioning applications to achieve sub-meter level accuracy using a stand-alone user receiver. To achieve the full-scale navigation parameters of position, velocity and attitude, GPS is often combined with the inertial navigation system (INS) to deliver navigation solutions in high update rate. However, GPS signals cannot always be tracked in difficult areas, which leads to an integration performance degradation because of INS sensor errors. Therefore, the authors propose a PPP-GPS/INS/odometer/map-matching integrated navigation system. The positioning performance based on the different International GNSS Service (IGS) products for train kinematic positioning applications is investigated. A field test was conducted on a specific low-density line to evaluate the proposed system. The test results confirm that the proposed multi-sensor navigation system can provide seamless navigation in both GPS available and unavailable situations with the accuracy of decimeter. Different IGS satellite orbits and clock offset products were used to generate the PPP results, and it was concluded that the IGS final products provide the best performance, with a distance root mean square (DRMS) of 0.138 m, and the IGS ultra-rapid product can also generate acceptable positioning solutions with a DRMS of 0.222 m.

Transverse cracks on bridge decks provide the path for chloride penetration and are the major reason for deck deterioration. For such reasons, collecting information related to the crack widths and spacing of transverse cracks are important. In this study, we focused on developing a data pipeline for automated crack detection using non-contact optical sensors. We developed a data acquisition system that is able to acquire data in a fast and simple way without obstructing traffic. Understanding that GPS is not always available and odometer sensor data can only provide relative positions along the direction of traffic, we focused on providing an alternative localization strategy only using optical sensors. In addition, to improve existing crack detection methods which mostly rely on the low-intensity and localized line-segment characteristics of cracks, we considered the direction and shape of the cracks to make our machine learning approach smarter. The proposed system may serve as a useful inspection tool for big data analytics because the system is easy to deploy and provides multiple properties of cracks. Progression of crack deterioration, if any, both in spatial and temporal scale, can be checked and compared if the system is deployed multiple times.

The paper is dedicated to discussing a method for ground multi-agent formation control in different obstacle environments. Use the data from the quadcopter vision system and the lidar sensor of the ground agent to create a global map. Then, we perform coordinate estimation of the ground agent by using the terrestrial agent’s odometer, GPS, and quadcopter vision system. Finally, we use the finite state machine theory to control the switching of ground multi-agent formation, and propose a strategy to avoid ground multi-agent collisions when the formation is switched. The effectiveness of this strategy is demonstrated through simulation experiments.

In this paper, we study the performance of a complementary filter with adaptive weights in a sensor fusion application for real-time localization of an omnidirectional field robot. The test-case robot is a large, four-wheel drive and steer (4WDS) construction vehicle with nonlinear internal dynamics and hydraulic driving and steering actuators. Our objective is to provide the vehicle's real-time controller with robust, smooth feedback that prevents unnecessary oscillations in steering, which can waste significant amounts of energy. We do so by assigning weights for measurements based on their consistency with the robot's motions. The calculations are based on two main data sources: (1) measured velocity vectors from wheel driving (odometer) and steering of the 4WDS test-case robot; and (2) data obtained from a differential global navigation satellite system on the absolute pose of the robot. We show that the sensor fusion is robust to the noise and single point failures of the sensors, while the maximum heading oscillations are reduced by 70%-95%, preserving the accuracy of the global positioning system. Moreover, we demonstrate the feasibility and efficacy of the real-time implementation of this filtering method in path-following control of the robot. acceptedVersion

Odometer is a good information source for land vehicle navigation because it can detect the velocity or mileage of a vehicle. Odometer scale factor, misalignment, and level arm with inertial measurement unit are the three sources of integration inaccuracy. However, they are difficult to be calibrated using a single procedure as they vary with many factors, such as the environment temperature, loading, and mounting factors. The online calibration method of the total odometer error and the odometer-aided inertial navigation system algorithm based on Coriolis Law were derived in this study. Vehicle test results indicated that the proposed calibration method can perform a good estimation of total odometer error. Navigation accuracy can be improved significantly with the calibrated odometer when the Global Positioning System declares loss-of-lock, which is approximately 1‰ of the mileage.

In recent years, autonomous driving technology has risen. Accurate positioning is an important part of self-driving cars. Global positioning system (GPS) and inertial navigation system (INS) are two commonly used navigation systems. Because GPS signals are easy to be interfered and IMU is easy to accumulate errors, they usually overcome each other’s shortcomings through Kalman filtering. The error model of the two systems is established. And add a speedometer to the system. Using extended Kalman filter (EKF) to build GPS/INS/ odometer/integrated navigation system is a practical data fusion method. This paper proposes a GPS/INS/ odometer integrated navigation system, which can provide accurate location and azimuth information. It meets the requirements of autonomous vehicle navigation. The experimental results show that the odometer can effectively assist the IMU even when the GPS signal is not available, and the algorithm is stable and reliable. The feasibility and effectiveness of the algorithm are verified by field test.

In this research, we propose a nonholonomic average speed measurement that newly recombines usable measurement in nonholonomic conditions to improve the heading accuracy of the vehicle Odometer/GPS/INS integrated navigation system. The average speed is less noise and the direction angle can be corrected more accurately because the information comes from data with long baselines. The advantages of this average speed is applied to nonholonomic measurements, which can improve the heading accuracy of the land vehicle navigation system. The proposed algorithm has been verified by Monte Carlo simulation. Verification results show improved heading accuracy and efficient applicability to navigation systems.

In the process of global positioning of robots, the errors of odometer caused by the abduction of robots and the errors of laser data matching results caused by ambient characteristics will lead to inaccurate Monte Carlo positioning. In this paper, an adaptive Monte Carlo localization algorithm based on position and attitude estimation is proposed. The real-time position and attitude are estimated by matching the scanning data of the laser sensor, and the real-time location is realized by combining the adaptive Monte Carlo algorithm. The results of simulation experiments on ROS show that the algorithm is superior to Monte Carlo algorithm in particle concentration and positioning accuracy, and has good robustness to the abduction problem of robots and the environment without obvious characteristics, which solves the positioning problem caused by the uncertainty of odometer.

Accurate positioning and orientation are of vital importance for autonomous vehicles. Global Navigation System(GPS) and Inertial Navigation System(INS) are two popular types of navigation systems and are usually integrated together to overcome each other’s defects. Based on the GPS/INS system, many other elements are introduced into it for accuracy improvement such as odometer, kinematic or dynamic models and so forth. This paper utilizes the dynamic characteristics of vehicles by constructing tire dynamic model and vehicle dynamic model. In this way, an integrated navigation system of GPS/INS/Odometer/DR(dead reckoning) is established by means of extended Kalman Filter(EKF), a practical and popular channel for data fusion. Field test is conducted to prove the algorithm feasible and valid. Test results indicate that this proposed GPS/INS/Odometer/DR integrated navigation system is able to provide accurate position and orientation information thus qualified for autonomous vehicles navigation. The test of GPS signal outage is also held and experiment results show that the proposed algorithm can also remain stable and reliable in case of GPS signal unavailability.

In navigation and motion control of an autonomous vehicle, estimation of attitude and heading is an important issue especially when the localization sensors such as GPS are not available and the vehicle is navigated by the dead reckoning (DR) strategies. In this paper, based on a new modeling framework an Extended Kalman Filter (EKF) is utilized for estimation of attitude, heading and gyroscope sensor bias using a low-cost MEMS inertial sensor. The algorithm is developed for accurate estimation of attitude and heading in the presence of external disturbances including external body accelerations and magnetic disturbances. In this study using the proposed attitude and heading reference system (AHRS) and an odometer sensor, a low-cost aided DR navigation system has been designed. The proposed algorithm application is evaluated by experimental tests in different acceleration bound and existence of external magnetic disturbances for a land vehicle. The results indicate that the roll, pitch and heading are estimated by mean value errors about 0.83%, 0.68% and 1.13%, respectively. Moreover, they indicate that a relative navigation error about 3% of the traveling distance can be achieved using the developed approach in during GPS outages.