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Mobile robot positioning, mapping, and navigation systems generally employ an inertial measurement unit to obtain the acceleration and angular velocity of the robot. However, errors in the internal and external parameters of an inertial measurement unit arising from defective calibration directly affect the accuracy of robot positioning and pose estimation. While this issue has been addressed by the mature internal parameters calibration method available for inertial measurement unit, external reference calibration method between the inertial measurement unit and the chassis of a mobile robot are lacking. This study addresses this issue by proposing a novel algorithm for internal parameter calibration of mecanum wheel omnidirectional mobile platform and external parameter calibration of mecanum chassis- inertial measurement unit based on principal component analysis and nonlinear optimization, which is designed for robots equipped with cameras, inertial measurement unit, mecanum wheels, and wheel speed odometers, and functions under the premise of accurate calibrations for the internal parameters of the inertial measurement unit and the internal and external parameters of the camera. All of the internal and external parameters calibrations are conducted using the robot's existing equipment without the need for additional calibration aids. The feasibility of the method is verified by its application to a mecanum wheel omnidirectional mobile platform. The proposed calibration method is thereby demonstrated to guarantee the accuracy of robot pose estimation.

Visual simultaneous localization and mapping (VSLAM) are essential technologies to realize the autonomous movement of vehicles. Visual-inertial odometry (VIO) is often used as the front-end of VSLAM because of its rich information, lightweight, and robustness. This article proposes the FPL-VIO, an optimization-based fast vision-inertial odometer with points and lines. Traditional VIO mostly uses points as landmarks; meanwhile, most of the geometrical structure information is ignored. Therefore, the accuracy will be jeopardized under motion blur and texture-less area. Some researchers improve accuracy by adding lines as landmarks in the system. However, almost all of them use line segment detector (LSD) and line band descriptor (LBD) in line processing, which is very time-consuming. This article first proposes a fast line feature description and matching method based on the midpoint and compares the three line detection algorithms of LSD, fast line detector (FLD), and edge drawing lines (EDLines). Then, the measurement model of the line is introduced in detail. Finally, FPL-VIO is proposed by adding the above method to monocular visual-inertial state estimator (VINS-Mono), an optimization-based fast vision-inertial odometer with lines described by midpoint and points. Compared with VIO using points and lines (PL-VIO), the line processing efficiency of FPL-VIO is increased by 3−4 times while ensuring the same accuracy.

Purpose Indoor localization is a key tool for robot navigation in indoor environments. Traditionally, robot navigation depends on one sensor to perform autonomous localization. This paper aims to enhance the navigation performance of mobile robots, a multiple data fusion (MDF) method is proposed for indoor environments. Design/methodology/approach Here, multiple sensor data i.e. collected information of inertial measurement unit, odometer and laser radar, are used. Then, an extended Kalman filter (EKF) is used to incorporate these multiple data and the mobile robot can perform autonomous localization according to the proposed EKF-based MDF method in complex indoor environments. Findings The proposed method has experimentally been verified in the different indoor environments, i.e. office, passageway and exhibition hall. Experimental results show that the EKF-based MDF method can achieve the best localization performance and robustness in the process of navigation. Originality/value Indoor localization precision is mostly related to the collected data from multiple sensors. The proposed method can incorporate these collected data reasonably and can guide the mobile robot to perform autonomous navigation (AN) in indoor environments. Therefore, the output of this paper would be used for AN in complex and unknown indoor environments.

The key technology to realize intelligent unmanned coal mining is the strapdown inertial navigation system (SINS); however, the gradual increase of cumulative error during the working process needs to be solved. On the basis of an SINS/odometer (OD)-integrated navigation system, this paper adds magnetometer (MAG)-aided positioning and proposes an SINS/OD/MAG-integrated shearer navigation system. The velocity observation equation is obtained from the speed constraints during shearer movement, and the yaw angle observation equation is obtained from the magnetometer output. The position information of the SINS output is calibrated using these two observations. In order to improve the fault tolerance of the integrated navigation system, an adaptive federated Kalman filter is established to complete the data fusion of the SINS. Experimental results show that the positioning accuracy of the SINS/OD/MAG-integrated navigation system is 75.64% and 74.01% higher in the east and north directions, respectively, than the SINS/OD-integrated navigation system.

The absolute three-dimensional position of a longwall shearer is fundamental to longwall mining automation. The positioning of the longwall shearer is usually realized by the inertial navigation system (INS) and odometer (OD). However, the position accuracy of this positioning approach gradually decreases over time due to the gyro drift. To further increase the positioning accuracy of the shearer, this paper proposes a positioning approach based on the INS and light detection and ranging (LiDAR). A Kalman filter (KF) model based on the observation provided by detecting hydraulic supports which are part of the longwall face, using the LiDAR, is established. The selection scheme of the point features is studied through a set of simulations. In addition, compared with that of the approach based on the INS and OD, the shearer positioning accuracy obtained using the proposed approach is higher. When the shearer moves along a 350 m track for 6 cutting cycles and lasts about 7.1 h, both east and north position errors can be maintained within 0.2 m and the height error within 0.1 m.

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.

To improve the mode and precision of wheelchair/nursing-bed automatic docking, a novel central embedded wheelchair/nursing-bed automatic docking method based on grid map is proposed. Firstly, Laplace operator and Iterative Closest Point (ICP) algorithm are used to filter and match Lidar point cloud, and the linear features of V-shaped artificial landmark are fitted by Split-merge method and least square method. Then Extended Kalman Filter (EKF) is used to fuse Inertial Measurement Unit (IMU) and odometer data to realize the localization of the bed and wheelchair. Meanwhile, the grid map is used for path planning. Based on the center-line of the two rear wheels and the angular bisector of V-shaped artificial landmark, the wheelchair pose is adjusted in real-time to ensure that the wheelchair gradually approaches the bed along the angular bisector of V-shaped artificial landmark. The yaw angle is reduced by using the improved Proportion Integration Differentiation (PID). 9 sets of experimental data, ie. (x, y, $\theta $ ) were collected at different starting positions during the docking process. The results show that the yaw angle of the wheelchair during the docking process is controlled within 2.5°, and the distance deviation between the final position and the ideal position of the wheelchair after docking is controlled within 0.02m. In the case of light interference with different luminous fluxes, the docking can still maintain good performance. The proposed docking algorithm has the robust performance of rapid response and low steady error, which can greatly improves the self-care ability of the bedridden elderly, and reduces the labor intensity of the nursing staff.

A graph-based particle filter bathymetric simultaneous localization and mapping (BSLAM) method is proposed to solve the oscillation problem of the trajectories estimated by particles when using a low precise vehicle motion model and obtain accurate navigation results for autonomous underwater vehicles (AUVs). A graph-based trajectory update method is proposed to update the trajectories stored in particles before particle weighting to weaken the influence of the low precise odometer model on the particle trajectories. A particle weighting method based on submap matching is proposed to improve the robustness of the particle filter. Besides, a graph-based map generation method is proposed to solve the map selection problem of the particle filtering theory. The performance of the proposed method is demonstrated using a simulated dataset and a field dataset collected from a sea trial. The results show that the proposed method is more accurate and effective compared with a state-of-art particle filter BSLAM method.

Drones may be more advantageous than fixed cameras for quality control applications in industrial facilities, since they can be redeployed dynamically and adjusted to production planning. The practical scenario that has motivated this paper, image acquisition with drones in a car manufacturing plant, requires drone positioning accuracy in the order of 5 cm. During repetitive manufacturing processes, it is assumed that quality control imaging drones will follow highly deterministic periodic paths, stop at predefined points to take images and send them to image recognition servers. Therefore, by relying on prior knowledge about production chain schedules, it is possible to optimize the positioning technologies for the drones to stay at all times within the boundaries of their flight plans, which will be composed of stopping points and the paths in between. This involves mitigating issues such as temporary blocking of line-of-sight between the drone and any existing radio beacons; sensor data noise; and the loss of visual references. We present a self-corrective solution for this purpose. It corrects visual odometer readings based on filtered and clustered Ultra-Wide Band (UWB) data, as an alternative to direct Kalman fusion. The approach combines the advantages of these technologies when at least one of them works properly at any measurement spot. It has three method components: independent Kalman filtering, data association by means of stream clustering and mutual correction of sensor readings based on the generation of cumulative correction vectors. The approach is inspired by the observation that UWB positioning works reasonably well at static spots whereas visual odometer measurements reflect straight displacements correctly but can underestimate their length. Our experimental results demonstrate the advantages of the approach in the application scenario over Kalman fusion, in terms of stopping point detection and trajectory estimation error. Xunta de Galicia | Ref. GRC2018/053 Ministerio de Economía, Industria y Competitividad | Ref. TEC2016-76465-C2-2-R

This article presents a novel sensor-fusion method for indoor vehicle tracking. The phase of the signals backscattered by a set of Ultra High Frequency-Radio Frequency Identification (UHF-RFID) reference tags spread in the scenario is combined with the information acquired by on-board low-cost kinematic sensors. The RFID data are acquired by the on-board reader, during the relative motion of the vehicle with respect to the static reference tags, by resembling a synthetic-array approach, with an advantageous reduction of the reference-tag spatial density. In particular, such phase samples are combined with the kinematic data collected by odometers, through a sensor-fusion approach. The method capability is investigated through a numerical analysis that accounts for the main system parameters. Then, the tracking capability is demonstrated through a measurement campaign in a laboratory test set with a UHF-RFID robot prototype equipped with commercial encoders. Experimental results show an average localization error of centimeter order in the estimation of medium-length trajectories by employing only two reference tags in a relatively small area. The proposed method does not need for any calibration procedure and can be implemented by commercial off-the-shelf (COTS) hardware.

Purpose The purpose of this paper is to propose a robust iterative LIDAR-based pose tracking method assisted by modified visual odometer to resist initial value disturbance and locate a robot in the environments with certain occlusion. Design/methodology/approach At first, an iterative LIDAR-based pose tracking method is proposed. The LIDAR information is filtered and occupancy grid map is pre-processed. The sample generation and scoring are iterated so that the result is converged to the stable value. To improve the efficiency of sample processing, the integer-valued map indices of rotational samples are preserved and translated. All generated samples are analyzed to determine the maximum error direction. Then, a modified visual odometer is introduced for error compensation. The oriented fast and rotated brief (ORB) features are uniformly sampled in the image. A local map which contains key frames for reference is maintained. These two measures ensure that the modified visual odometer is able to return robust result which compensates the error of LIDAR-based pose tracking method in the maximum error direction. Findings Three experiments are conducted to prove the advantages of the proposed method. The proposed method can resist initial value disturbance with high computational efficiency, give back credible real-time result in the environment with abundant features and locate a robot in the environment with certain occlusion. Originality/value The proposed method is able to give back real-time pose tracking results with robustness. The iterative sample generation enables the robot to resist initial value disturbance. In each iteration, rotational and translational samples are separately generated to enhance computational efficiency. The maximum error direction of LIDAR-based pose tracking method is determined by principle component analysis and compensated by the result of modified visual odometer to give back correct pose in the environment with certain occlusion.

SUMMARYSelf-localization in highly dynamic environments is still a challenging problem for humanoid robots with limited computation resource. In this paper, we propose a dual-channel unscented particle filter (DC-UPF)-based localization method to address it. A key novelty of this approach is that it employs a dual-channel switch mechanism in measurement updating procedure of particle filter, solving for sparse vision feature in motion, and it leverages data from a camera, a walking odometer, and an inertial measurement unit. Extensive experiments with an NAO robot demonstrate that DC-UPF outperforms UPF and Monte–Carlo localization with regard to accuracy.

This paper proposes a fine semantic mapping method using dense segmentation network (DS-Net) to obtain good performance of semantic mapping fusion. First, the RGB image and the depth image are used to generate a dense indoor scene map via the state-of-the-art dense SLAM (ElasticFusion). Then, the DS-Net is constructed based on DenseNet’s dense connection to perform precise semantic segmentation on the input RGB image. Finally, the long-term correspondence is established between the indoor scene map and the landmarks using continuous frames both in the visual odometer and in loop detection, and the final semantic map is obtained by fusing the indoor scene map with the semantic predictions of the RGB-D video frames of multiple angles. Experiments were performed on the NYUv2, PASCAL VOC 2012, CIFAR10 datasets and our laboratory environments. Results show that our method can reduce the error in dense map construction and obtain good semantic segmentation performance.

High-precision navigation of the underground excavating machine is the core of realizing safe excavating and autonomous control, but it is difficult to realize high-precision navigation for the underground excavating machine. The integration of GNSS and inertial navigation system are generally adopted for the high-precision navigation of the ground equipment, which can provide high-precision navigation for a long time. However, the GNSS signals cannot be used in underground operation, so the navigation scheme applied to the ground cannot be used underground. At the same time, the velocity of the underground excavation machine is very slow and the operation time is very long, which makes it impossible to use pure inertial navigation scheme for navigation. Because the inertial navigation error accumulates with time, if the operation time is too long, the navigation accuracy cannot be guaranteed. The integration of inertial navigation and odometer is considered as a reliable scheme, but simulation and experimental analysis show that the traditional integration of inertial navigation and odometer cannot meet the operation requirements of the underground excavating machine in the presence of serious slipping and measurement noises. This paper proposes an integration of fully damped inertial navigation/odometer to provide long-term high-precision orientation navigation information for mining equipment. The advantage of the fully damped inertial navigation/odometer integration approach is that it can maintain high accuracy of orientation measurement in one week or longer under the condition of sensor drift error and odometer measurement error. Simulation and field tests are carried out to validate the performance of the proposed approach.

Visual odometry is an essential component in robot navigation and autonomous driving; however, visual sensors are vulnerable in fast motion or sudden illumination changes. This weakness can be compensated with inertial measurement units (IMUs), which maintain the short-term motion when visual sensing becomes unstable and enhance the quality of estimated motion with inertial information. An omnidirectional multi-view visual odometry (ROVO) has recently demonstrated superior performance and stability with the unceasing feature observation of the omnidirectional setup; however, the shortcomings of visual odometry remain. This letter introduced an omnidirectional visual-inertial odometry system (ROVINS) that could seamlessly integrate the inertial information into the omnidirectional visual odometer algorithm: (a) The soft relative pose constraints from the inertial measurement are first added to the pose optimization formulation, which enables blind motion estimation when all visual features are lost; (b) Using the prediction results from the estimated velocity, the visual features in tracking are initialized, resulting in feature tracking that is more robust to visual disturbances. The experimental results showed that the proposed ROVINS algorithm outperforms the vision-only algorithm by a significant margin.

In the used car market, mileage is one of the principal criteria used for evaluating the overall condition of a vehicle. For this reason, mileage fraud continues to occur. Futhermore, the number of malicious brokers who obtain a monetary advantage by manipulating vehicle mileage is increasing. As many used cars with manipulated mileage are sold each year, buyers have suffered significant monetary damages. Although the number of mileage fraud reports has remained steady, governments and OEMs have no technical countermeasures to prevent it, beyond asking used car buyers to pay careful attention when purchasing used cars. This paper classifies odometer system architectures according to the types of sensors and controllers used to measure and transmit vehicle speed, and defines the assets to be protected in a secured odometer. Based on this classification, we analyze potential security threats related to mileage fraud. Finally, we propose realistic security requirements to prevent mileage fraud, within a resource constrained automotive controller environment.

Collaborative navigation is the key technology for multimobile robot system. In order to improve the performance of collaborative navigation system, the collaborative navigation algorithms based on odometer/vision multisource information fusion are presented in this paper. Firstly, the multisource information fusion collaborative navigation system model is established, including mobile robot model, odometry measurement model, lidar relative measurement model, UWB relative measurement model, and the SLAM model based on lidar measurement. Secondly, the frameworks of centralized and decentralized collaborative navigation based on odometer/vision fusion are given, and the SLAM algorithms based on vision are presented. Then, the centralized and decentralized odometer/vision collaborative navigation algorithms are derived, including the time update, single node measurement update, relative measurement update between nodes, and covariance cross filtering algorithm. Finally, different simulation experiments are designed to verify the effectiveness of the algorithms. Two kinds of multirobot collaborative navigation experimental scenes, which are relative measurement aided odometer and odometer/SLAM fusion, are designed, respectively. The advantages and disadvantages of centralized versus decentralized collaborative navigation algorithms in different experimental scenes are analyzed.

To improve the accuracy of attitude and heading reference systems for moving vehicles, an effective orientation estimation method is proposed. The method uses an odometer, a low-cost magnetic, angular rate, and gravity sensor. This study addresses the problems of non-orthogonal error, carrier magnetic field interference and calibration to obtain accurate, long-term, stable magnetic field strength. A neural network fusion 12-parameter ellipse fitting method is proposed to eliminate the soft magnetic field and hard magnetic field interference. The interference to the accelerometer from linear acceleration is eliminated by using an odometer and a gyroscope, and the high-frequency noise from the accelerometer is eliminated by using a low-pass filter. An improved method to evaluate vehicle attitude is proposed and utilized to compensate for filtered accelerometer measurement when the vehicle is moving at a uniform, accelerate and steering state. The proposed method uses an effective adaptive Kalman filter based on the error state model to reduce dynamic perturbations. Filter gain is adaptively tuned under different moving modes by adjusting the noise matrix. The effectiveness of the algorithm is verified by experiments and simulations in multiple operating conditions.

To deal with the low accuracy of positioning for mobile robots when only using visual sensors and an IMU, a method based on tight coupling and nonlinear optimization is proposed to obtain a high-precision visual positioning scheme by combining measured value of the preintegrated inertial measurement unit (IMU) and values of the odometer and characteristic observations. First, the preprocessing part of the observation data includes tracking of the image data and the odometer data, and preintegration of IMU data. Second, the initialization part of the above three sensors includes IMU preintegration, odometer preintegration, and gyroscope bias calculation. It also includes the alignment of speed, gravity, and scale. Finally, a local BA (bundle adjustment) joint optimization and global graph optimization are established, so as to obtain more accurate positioning results.

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.

The traditional pedestrian navigation system that uses zero velocity update algorithm cannot calculate traveled distance accurately or observe the heading error. A new model called body odometer (BOR) that consists of a step length model and a correction factor is proposed to obtain precise single step length for dead reckoning. A BOR/MINS integrated framework uses the difference between micro electro mechanical inertial navigation system (MINS) calculated distance and single step length, as a new observation to estimate the correction factor and compensate navigation errors via a Kalman filter. To eliminate the heading error accumulation, a new gyro drift reduction method that combines heuristic drift reduction method and complementary filter is presented. The 200 m straight line experiments show that the calculated distance by BOR/MINS integrated method is much closer to the real distance with the average error percentage of 0.24%. Three differently designed trajectories’ experiments show that the proposed method has a higher match degree with the real trajectories and the positioning error with respect to the total traveled distance is less than 0.6%.

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.

Odometer and nonholonomic constraints are crucial external aids for inertial navigation systems (INSs) to achieve desirable positioning accuracy for pipeline inspection gauges (PIGs). Exploiting the maximum potential of these constraints requires the accurate knowledge of the inevitably existing misalignment in the attitudes of the inertial measurement unit (IMU) with respect to the host vehicle (i.e., the IMU mounting angle relative to the PIG). In this paper, we address the mounting angle calibration of a PIG without using sophisticated equipment. Instead, the proposed method only requires the PIG to be placed on a simple leveled frame and rotated about its longitudinal axis. In this case, the existing IMU mounting angles will make the IMU’s pitch and heading angles vary with rotation. Closed-form analytic solutions for the IMU’s attitude angles are obtained under this specific condition and show that the magnitude and phase of the IMU’s pitch and heading angles have a linear relation with the corresponding pitch and heading misalignment angles. Then, a method for the misalignment angle estimation is presented based on this principle. Simulations are performed to validate the proposed method and investigate the related error sources that influence the estimation accuracy. The results of the real tests indicate that the mounting angles can be accurately estimated and compensated through this simple and convenient calibration.

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.

International audience; This paper deals with the online estimation of the geometric and kinematic parameters of a wheeled mobile robot, with the objective of its precise navigation. To do this, the implementation of an algorithm for estimating these parameters is proposed. It is based on a quadratic criterion to be minimized, which is a function of the difference between the measured and estimated robot pose. The estimator inputs external measurements of robot positions and speeds and proprioceptive inertial and odometer measurements, and outputs an estimate of model parameters. Its expression does not depend on the position of the robot relative to the path to be followed and its possible tracking errors, nor on the speed of the tracking. The experimental implementation carried out on a real bi-directional container truck under realistic operating conditions has shown its performance.

Fraud in automobile odometers is a billion dollar problem worldwide. The technical requirements behind any solution for this problem go through the perennial, secure, and immutable registration of vehicular data. Such requirements immediately raise the question of how blockchain technology could be used for this purpose. In this article, the requirements, design choices, and construction details of a blockchain architecture for vehicular odometers are presented and discussed. The architecture advances the state of the art by adopting a more efficient consensus algorithm than the traditional ones, such as proof of work. Results from a prototype proof ofconcept of the proposed architecture validate the solution, which is able to validate a new block in the blockchain every two seconds. Applicability in real scenarios is one step ahead and very promising.

Thermal images produced by the long-wavelength infrared (LWIR) camera are robust and independent from environmental illumination change. They can help the standard visible light camera working under the complicated environmental condition. Breaking through the traditional stereo multi-spectral sensor consisting of a visible-light camera and a LWIR camera, a novel architecture of large field of view (FOV) cooperated infrared and visible spectral sensor for visual odometry is proposed. The novel sensor is equipped with two visible cameras, four infrared cameras covering 120 degrees FOV in horizontal under both bands. Distribution of cameras and related peripheral devices are specifically designed which makes the sensor's volume less than 100 cm (length) × 10 cm (height) × 10 cm (width). The sensor's cameras calibration, distortion correction and measurement principle are elaborated. Feature-based method for visible and multi-windowed optimization-based image alignment for infrared is designed for the visual odometer based on the different imaging mechanism and distribution of cameras in the sensor. The frames and estimated poses management from both bands are proposed. Moreover, all proposed methodologies can be implemented in the sensor's embedded processor. The electrical power consumption is only 12W. Experiments of the sensor's evaluation are performed, experimental results show that large FOV cooperated multi-spectral cameras can efficiently improve the robustness of visual odometry. The real-time performance of the sensor is higher than 10fps with disparity map construction under both bands.

In-motion alignment of Strapdown Inertial Navigation Systems (SINS) without any geodetic-frame observations is one of the toughest challenges for odometer aided navigation system. It is difficult to obtain an accurate attitude within a short time using coarse alignment under intense maneuvering condition, so one of the main obstacles during in-motion alignment for SINS/OD is to estimate an accurate attitude during fine alignment process under large azimuth misalignment angle. However, most current in-motion alignment methods were based on linear error model. In this paper, an OD aided in-motion alignment method based on backtracking scheme for large misalignment angles is proposed. Three improvements were made as the following: (1) A continuous nonlinear 16-dimensional (16D) SINS/OD error model is derived. (2) We proposed a backtracking-based Extended Kalman filter (EKF) and (3) a strict reverse navigation method is utilized in the backward process. Experimental results illustrated that the proposed method can autonomously improve the estimated attitude accuracy under the condition of large misalignment angle.

In order to effectively reduce the impact of Global Navigation Satellite System (GNSS) attacks while providing mobile terminals with credible navigation and positioning results, this paper proposes a credible navigation algorithm for GNSS attack detection using an auxiliary sensor system. Based on a credible Kalman filter and measurement information provided by the auxiliary sensor system on mobile terminals, the proposed algorithm can verify the credibility of the GNSS positioning result and determine whether it has suffered from a GNSS attack using the credible verification window and the credible verification threshold. According to the verification results, the algorithm can adaptively select an updated model for measurement correction and achieve a credible navigation result. The algorithm proposed in this paper has been verified on a self-developed mobile terminal, and the experimental results show that the algorithm can provide credible navigation and positioning services for mobile terminals in the context of GNSS attacks.

In this article, a multisensor joint localization system is proposed based on modified cubature Kalman filtering, which aims to improve the accuracy of state estimation under a moderate computational burden in the presence of high process noise. Specifically, first, the covariance of process noise is matched based on adaptive filtering. The inertial measurement unit (IMU), odometer (ODM), and ultra-wideband (UWB) information acquired by the associated sensors is then employed to augment the system state and are fused to lower the influence of process noise. In the presented localization setting, all sensors (IMU/ODM/UWB) are set to work in parallel under the federated Kalman filter (FKF) framework, which can correct the cumulative error of the internal sensor and and can improve the computational efficiency. Two sets of numerical simulations were performed to show that the proposed method can obtain accurate state estimation with a slightly increased computational burden.

This paper proposes and implements a lightweight, “real-time” localization system (SORLA) with artificial landmarks (reflectors), which only uses LiDAR data for the laser odometer compensation in the case of high-speed or sharp-turning. Theoretically, due to the feature-matching mechanism of the LiDAR, locations of multiple reflectors and the reflector layout are not limited by geometrical relation. A series of algorithms is implemented to find and track the features of the environment, such as the reflector localization method, the motion compensation technique, and the reflector matching optimization algorithm. The reflector extraction algorithm is used to identify the reflector candidates and estimates the precise center locations of the reflectors from 2D LiDAR data. The motion compensation algorithm predicts the potential velocity, location, and angle of the robot without odometer errors. Finally, the matching optimization algorithm searches the reflector combinations for the best matching score, which ensures that the correct reflector combination could be found during the high-speed movement and fast turning. All those mechanisms guarantee the algorithm’s precision and robustness in the high speed and noisy background. Our experimental results show that the SORLA algorithm has an average localization error of 6.45 mm at a speed of 0.4 m/s, and 9.87 mm at 4.2 m/s, and still works well with the angular velocity of 1.4 rad/s at a sharp turn. The recovery mechanism in the algorithm could handle the failure cases of reflector occlusion, and the long-term stability test of 72 h firmly proves the algorithm’s robustness. This work shows that the strategy used in the SORLA algorithm is feasible for industry-level navigation with high precision and a promising alternative solution for SLAM.

The information we access on the Internet appears immediately but usually lives far away. The Desktop Odometer is a device that shows users the distance they travel when browsing the web by tracking the total miles between their current location and the server from which they are requesting information. In this work, we investigated internet infrastructures by designing and producing Desktop Odometers, selling them on Amazon.com, and receiving customer reviews. We present our analysis of customer reviews which reveal how customers describe their understandings of internet infrastructures after using the device. We also recount our RtD approach to making the device; we describe frictions we encountered when navigating other opaque infrastructures in our fabrication process, such as the Google Play store. Finally, we reflect on our use of Amazon.com and customer reviews as a method to engage participants in discussion about internet infrastructure through the sale and review process.

Electric vehicles (EVs) have emerged as the green energy alternative for conventional vehicles. While various governments promote EVs, people feel “range anxiety” because of their limited driving range or charge capacity. A limited number of charging stations are available, which results in a strong demand for predicting energy consumed by EVs. In this paper, machine learning (ML) models such as multiple linear regression (MLR), extreme gradient boosting (XGBoost), and support vector regression (SVR) were used to investigate the total energy consumption (TEC) by the EVs. The independent variables used for the study include changing real-life situations or external parameters, such as trip distance, tire type, driving style, power, odometer reading, EV model, city, motorway, country roads, air conditioning, and park heating. We compared the ML models’ performance along with the error analysis. A pairwise correlation study showed that trip distance has a high correlation coefficient (0.87) with TEC. XGBoost had better prediction accuracy (~92%) or R2 (0.92). Trip distance, power, heating, and odometer reading were the most important features influencing the TEC, identified using the shapley additive explanations method.

The detection of track geometry parameters is essential for the safety of high-speed railway operation. To improve the accuracy and efficiency of the state detector of track geometry parameters, in this study we propose an inertial GNSS odometer integrated navigation system based on the federated Kalman, and a corresponding inertial track measurement system was also developed. This paper systematically introduces the construction process for the Kalman filter and data smoothing algorithm based on forward filtering and reverse smoothing. The engineering results show that the measurement accuracy of the track geometry parameters was better than 0.2 mm, and the detection speed was about 3 km/h. Thus, compared with the traditional Kalman filter method, the proposed design improved the measurement accuracy and met the requirements for the detection of geometric parameters of high-speed railway tracks.

A well-known, crucial problem for indoor positioning of mobile agents (e.g., robots) equipped with exteroceptive sensors is related to the need to deploy reference landmarks in a given environment. Normally, anytime a landmark is detected, an agent estimates its own location and attitude with respect to landmark position and/or orientation in the chosen reference frame. When instead no landmark is recognized, other sensors (e.g., odometers in the case of wheeled robots) can be used to track the agent position and orientation from the last detected landmark. At the moment, landmark placement is usually based just on common-sense criteria, which are not formalized properly. As a result, positioning uncertainty tends to grow unpredictably. On the contrary, the purpose of this paper is to minimize the number of landmarks, while ensuring that localization uncertainty is kept within wanted boundaries. The developed approach relies on the following key features: a dynamic model describing agents’ motion, a model predicting the agents’ paths within a given environment and, finally, a conjunctive normal form formalization of the optimization problem, which can be efficiently (although approximately) solved by a greedy algorithm. The effectiveness of the proposed landmark placement technique is first demonstrated through simulations in a variety of conditions and then it is validated through experiments on the field, by using non-Bayesian and Bayesian position tracking algorithms.

This paper presents detailed derivations and further explanations of the state transformation extended Kalman filter (ST-EKF) from the common frame error definition perspective. Both the system error and measurement models of strapdown inertial navigation system (SINS)/Odometer (OD) tightly-coupled navigation are derived based on the ST-EKF. Both theoretical analysis and test results indicate the effectiveness of the proposed velocity error definition on mitigating the covariance-inconsistency problem that is caused by the specific force calculation error in the EKF state transition matrix. The excellent covariance-consistency of the ST-EKF makes it not necessary to remove gyro and accelerometer bias errors from the initial alignment Kalman filter states. This phenomenon can ensure the subsequent integrated navigation performance. Single-position ground SINS alignment experiments by using a navigation grade inertial measurement unit (IMU) showed that the estimated yaw angle from the conventional 15-state EKF slowly diverged over time. Furthermore, this phenomenon became more distinct when the initial yaw angle error was larger. In contrast, the estimated yaw angle from the proposed 15-state ST-EKF was significantly more stable and less degraded by large initial yaw angle errors. Long- distance land-vehicle SINS/OD integrated navigation tests also exhibited the ST-EKF's higher positioning and heading accuracy than the EKF.

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.

Mobile Mapping Systems (MMS) with Inertial Navigation System / Global Navigation Satellite System (INS/GNSS) and mapping sensors have been widely developed in recent years. However current systems and results are still prone to errors, especially in GNSS-denied or multipath environments. To provide robust and stable navigation information, particularly for mapping in long-term GNSS-denied environments, we propose a semi-tightly coupled integration scheme which integrates INS/GNSS with grid-based Simultaneous Localization and Mapping (SLAM). Although traditional SLAM using LiDAR can map the GNSS-denied environment efficiently, it is only in local localization. The proposed integration scheme is based on the Extended Kalman Filter (EKF) with motion constraints. In this scheme, a measurement model for grid-based SLAM is aided by the heading and velocity information. A special innovation of this scheme is the improved fusion of GNSS/INS with the use of grid-based SLAM serves like virtual odometer and virtual compass, thus gaining reliable measurements and error models to maintain good performance during INS-only mode. In addition, the initial values for example position and heading, are given to solve global localization and loop closure problems in SLAM. Finally, a smoothing and multi-resolution map strategy are applied offline to increase the robustness and performance of the proposed grid-based SLAM. Evaluation based on experimental data shows the significant improvement by the proposed semi-tightly coupled integration scheme with low-cost INS/GNSS and LiDAR, which is able to achieve 1–2 m’ accuracy in terms of positioning and mapping. An approximately 60% improvement was achieved during long-term GNSS-denied environments using the proposed integration scheme.

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.

Cameras and encoders are widely used in mobile robots, and extrinsic parameter calibration of these sensors is crucial in practical performance. The existing approaches mainly rely on manual measurements, accurate computer-aided design (CAD) models, or carefully designed artificial landmarks. This paper presents a novel approach for automatically calibrating the extrinsic parameters of the robotic camera–encoder system. The approach first calculates a coarse estimation of the external parameters as well as the scale of the visual system, via free-scale hand–eye calibration of the camera and odometer. However, the coarse calibration result and scale of the visual system do not satisfy the accuracy requirement for further mobile robots’ applications such as localization and navigation. A nonlinear optimization algorithm that considers both bundle adjustment and odometer measurement error functions is developed to refine the extrinsic parameter calibration result. This coarse-fine approach is computationally efficient and can achieve online calibration during the vehicle motion automatically. Furthermore, it can realize the calibration without using any artificial landmark or prior knowledge about CAD models. Finally, comparisons to other classic calibration approaches are performed with a series of simulations and experiments to illustrate the effectiveness of the approach.

Longwall shearer positioning with an inertial navigation system (INS) aided by an odometer is the key technology for automation of the longwall coal mining process. In order to improve the shearer positioning accuracy without using external sensors, the closing path motion characteristics of a shearer working in a longwall mining face was summarized by analyzing the longwall mining method. A closing path optimal estimation model was proposed based on the motion characteristics of the shearer and a Kalman filter (KF). Through simulation tests, it was verified that the model reduced the influence of heading angle drift of an INS on the shearer positioning accuracy without using external sensors. The shearer positioning accuracy based on the circular error probable was improved by approximately an order of one under the effect of the optimal estimation model. This was beneficial for reducing the large positioning error of the shearer in a longwall mining face during each cutting cycle for approximately 1 h. Note to Practitioners —This paper was motivated by the problem of longwall coal mining automation, especially positioning of longwall shearer, but it also applies to other mobile mining equipment. Longwall shearer positioning with an INS aided by an odometer has been regarded as an effective technological approach to longwall mining automation by coal mining workers and researchers. However, the positioning accuracy is largely subjected to the performance of the INS. This paper proposed a closing path optimal estimation model based on the motion characteristics of the shearer and a KF. Through simulation tests, it was verified that the model improved the positioning accuracy of the shearer by approximately an order of 1. This was beneficial for astricting the large positioning error of the shearer in a longwall mining face during each cutting cycle for approximately 1 h. In the future research, we will address the field tests of the closing path optimal estimation model and extending other mining applications.

The use of Unmanned Aerial Vehicles (UAVs) in many commercial and emergency applications has the potential to dramatically alter several industries, and, in the process, change our attitudes regarding their impact on our daily lives activities. The navigation system of these UAVs mainly depends on the integration between the Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS) to estimate the positions, velocities, and attitudes (PVT) of the UAVs. However, GNSS signals are not always available everywhere and therefore during GNSS signal outages, the navigation system performance will deteriorate rapidly especially when using low-cost INS. Additional aiding sensors are required, during GNSS signal outages, to bound the INS errors and enhance the navigation system performance. This paper proposes the utilization of two sensors (Hall-magnetic and Air-Mass flow sensors) to act as flying odometer by estimating the UAV forward velocity. The estimated velocity is then integrated with INS through Extended Kalman Filter (EKF) to enhance the navigation solution estimation. A real experiment was carried out with the 3DR quadcopter while the proposed system is attached on the top of the quadcopter. The results showed great enhancement in the navigation system performance with more than 98% improvement when compared to the free running INS solution (dead-reckoning).

A low-cost IMU and odometer integration system is developed in this paper for underground pipeline mapping. A tightly coupled integration between IMU and odometer is implemented to decrease the error caused by the odometer installation attitude error and scale factor error. Besides this, a novel approach to this application of using a Robust Kalman filter is developed to remove the effect of odometer measurement outliers due to the wheel-slip. Compared with the loosely coupled integration method, the use of loosely coupled integration with scale factor correction, tightly coupled integration and tightly coupled with Robust Kalman filter provide a horizontal position improvement of 11%, 41% and 43%, respectively. Similarly, the height accuracy is improved by 14%, 50% and 57%. Moreover, after applying the Robust Kalman filter, the positioning error caused by wheel-slip is reduced to 0.61 m in the horizontal plan, and 0.11 m in the height.

This article proposes a method for sensor fusion between odometers, gyroscope, accelerometer, magnetometer and visual landmark localization system. The method is designed for estimation of all 6 degrees of freedom (both translation and attitude) of a wheeled robot moving in uneven terrain. The fusion method is based on continuous estimation of the mean square error of each estimated value and allows different sampling rates of each sensor. Due to the simple implementation, it is suitable for real-time processing in the low-cost hardware. In order to evaluate the precision of the estimated position, stochastical models of sensors (with parameters matching real hardware sensors) were used and random trajectories were simulated. The virtual experiments showed that the method is resistant to the failure of any sensor except the odometers; however, each sensor provides improvement in the resultant precision.

In this paper, we present a novel tightly-coupled probabilistic monocular visual-odometric Simultaneous Localization and Mapping algorithm using wheels and a MEMS gyroscope, which can provide accurate, robust and long-term localization for the ground robot moving on a plane. Firstly, we present an odometer preintegration theory that integrates the wheel encoder measurements and gyroscope measurements to a local frame. The preintegration theory properly addresses the manifold structure of the rotation group SO(3) and carefully deals with uncertainty propagation and bias correction. Then the novel odometer error term is formulated using the odometer preintegration model and it is tightly integrated into the visual optimization framework. Furthermore, we introduce a complete tracking framework to provide different strategies for motion tracking when (1) both measurements are available, (2) visual measurements are not available, and (3) wheel encoder experiences slippage, which leads the system to be accurate and robust. Finally, the proposed algorithm is evaluated by performing extensive experiments, the experimental results demonstrate the superiority of the proposed system., Comment: 13 pages, 31 figures

Recently, the Rao-Blackwellized particle filter (RBPF) has been used to solve the problem of simultaneous localization and mapping (SLAM). Using the odometer information of robot to calculate the proposed distribution requires a number of sampled particles, which increases the calculation complexity in the RBPF operation. In this paper, we integrate the odometer measurement and sensor observation into the proposed distribution, effectively reducing the particle sample scale. To reduce the inconsistency in the map model caused by the cumulative error of the odometer information of robot, we applied a gradient descent algorithm to fuse the sensor data to obtain the real-time attitude angle. This combination method, based on the robot operation system (ROS), runs on a platform of self-built mobile robot equipped with a laser rangefinder. The experimental results show that this method can realize the online real-time high-precision grid map which provides a new approach for robot navigation and SLAM.

This paper proposes a Simultaneous Calibration and Navigation (SCAN) algorithm of a multiple Ultrasonic Local Positioning Systems (ULPSs) that cover an extensive indoor area. The idea is the development of the same concept than SLAM (Simultaneous Localization and Mapping), in which a Mobile Robot (MR) estimates the map while it is navigating. In our approach, the MR calibrates the beacons of several ULPSs while it is moving inside the localization area. The concept of calibration is the estimation of the position of the beacons referenced to a known map. The scenario is composed of some calibrated ULPSs that we denote as Globally Referenced Ultrasonic Local Positioning Systems (GRULPSs) that are located in strategic points like entrances covering the start and the end of a possible trajectory in the environment. Additionally, there are several non-calibrated ULPSs named Locally Referenced Ultrasonic Local Positioning Systems (LRULPSs) that are placed around the localization area. The proposal uses a MR with odometer for calibrating the beacons of the LRULPSs while it is navigating on their coverage area and go from one GRULPS to another. The algorithm is based on multiple filters running in parallel (one filter for each LRULPS and another one for the GRULPSs) that estimate the global and local trajectories of the MR (one trajectory for each local reference system of the LRULPSs) fusing the information related to the Ultrasound Signals (US) and the odometer of the MR. The position of the beacons of the LRULPSs are obtained by a transformation vector for each LRULPS that converts the local coordinates to the global reference system. This transformation vector is calculated using several points of a local trajectory and the corresponding points of the global one. The method is independent of the type of filter, provided that it works properly with non-linear systems and possibly non-Gaussian noise. Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and H-∞ Filter have been tested, in simulations and real experiments, in order to compare their performance in this case.

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.

An inertial navigation system (INS) aided by odometers and a series of reference points has been extensively used to determine the location of pipeline inspection gauges (PIGs) in long-range gas and oil pipeline inspection. However, little attention has been paid to short-range urban underground pipeline surveying and the use of a low-cost inertial measurement unit (IMU) for location determination. The positioning performance of PIGs using low-cost microelectromechanical systems (MEMS) INS for short-range urban underground pipeline surveying applications has seldom been comprehensively evaluated. In this paper, we analyzed the positioning accuracy of a PIG based on a MEMS IMU and odometer through a real pipeline surveying case study. Numerous pipeline surveying cases, covering different pipeline lengths, pipe conditions, and surveying times, are included to achieve a practical conclusion with respect to the surveying accuracy. The results demonstrated that the maximum measurement repeatability errors of PIGs based on MEMS IMU and odometer do not exceed 0.25% and 0.1% of the pipeline length in the horizontal and vertical directions, respectively. These results not only provide powerful support for the PIG developers but also provide the expected PIG accuracy in urban underground pipeline surveying by using low-cost IMUs.

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.