Integrated Positioning System Research Articles

ROS-based multi-sensor integrated localization system for cost-effective and accurate indoor navigation system

ROS-based multi-sensor integrated localization system for cost-effective and accurate indoor navigation system

A simulated fusion localization algorithm with adaptive error covariance matrix for closed corridor seamless positioning

Due to the blocking and reflection of buildings, the received Beidou satellite navigation signal (BDS) in the closed corridor will be getting weaker, which results in poor positioning accuracy or positioning failure. Introducing Ultra-Wide Band (UWB) positioning technology and establishing BDS/UWB integrated positioning system is an effective method to achieve seamless positioning. The positioning accuracy obtained by the Least Square-Kalman Filter (LS-KF) algorithm is below m level. However, the prior error covariance matrix of the KF correction process is easily contaminated, which affects the proportion of system observations and state prediction values to the optimal position estimation. A LS-KF fusion positioning algorithm based on adaptive error covariance matrix is proposed, the algorithm adjusts the state prior error covariance matrix by constructing an adaptive factor, improves the Kalman gain and the optimal estimation of the position, balances the trust of the filter estimation value to the observation value and the state prediction value of the integrated positioning system. Theoretical analysis and experimental results show that compared with LS-KF, EKF, UKF and improved LS-KF algorithm, the proposed LS-AKF algorithm not only has higher positioning accuracy, stronger anti-interference ability, but also has faster convergence speed.

Perancangan Perbaikan Positioning Produk Kemeja Schouten Berdasarkan Perceptual Mapping dengan Metode Multidimensional Scaling

This study aims to identify the attributes of the shirt using perceptual mapping and the resulting map will be the basis for recommendations for improvements in the positioning of Schouten shirt products. Positioning is carried out using the Multidimensional scaling method which produces a Perceptual mapping from the results of the perceptions of 121 respondents who are shirt consumers on 11 attributes on each shirt brand. From the results of the Perceptual Mapping, we get the priority attributes of improvement and Schouten's closest competitor, Sipolos, based on Euclidean distance. In addition, based on the results of the Perceptual Mapping, a positioning improvement plan for Schouten shirt products was made with an integrated positioning system design for Schouten based on the shirt's attributes which are a unique value proposition to improve positioning and consumer perceptions of Schouten. There is an attribute of the Schouten shirt product which is a priority for improvement, namely the variety of models to improve consumer perceptions of Schouten and increase brand awareness of the Schouten brand.
 Keywords: Positioning, Multidimensional Scaling, Perceptual Mapping, Shirt Product, Schouten

Wi-Fi RTT/Encoder/INS-Based Robot Indoor Localization Using Smartphones

With the rapid development of robotics, miniaturized and inexpensive robots have gradually entered the public's field of vision. Smartphone-based robots have the potential to provide high-precision indoor localization, and they are easy to promote and apply. In this paper, we propose a tight integrated positioning system based on Wi-Fi round trip time (RTT), encoders, and the inertial measurement unit (IMU) for robot indoor localization using smartphones. In our approach, we first design an error-state Kalman Filter (ESKF) to fuse the inertial information from the IMU with the measurements from the encoders to suppress the errors accumulated by the inertial navigation system (INS). Second, the Wi-Fi RTT-based localization method is implemented through an adaptive extended Kalman filter (AEKF) to fuse the ranging information. Finally, to overcome the shortcomings of the long-time drift of the INS and the instability of the Wi-Fi RTT-based localization system, we implement a tight integrated positioning system, which obtains the optimal estimation of the INS positioning error by using the Wi-Fi RTT ranging values as filtering constraints and smooths the INS positioning error through the Rauch-Tung-Striebel (RTS) algorithm. Experimental results show that compared with the Wi-Fi RTT-based method under line of sight condition (LOS) and non-line of sight condition (NLOS), the mean positioning error of the proposed method is improved by 54.62% and 58.38%, respectively, while compared with the INS/Encoder method, those is improved by 57.48% and 33.04%, respectively.

KF-Loc: A Kalman Filter and Machine Learning Integrated Localization System Using Consumer-Grade Millimeter-Wave Hardware

With the ever-increasing demands of e-commerce, the need for smarter warehousing is increasing exponentially. Such warehouses require industry automation beyond Industry 4.0. In this work, we use consumer-grade millimeter-wave (mmWave) equipment to enable fast, and low-cost implementation of our localization system. However, the consumer-grade mmWave routers suffer from coarse-grained channel state information due to cost-effective antenna array design limiting the accuracy of localization systems. To address these challenges, we present a machine learning (ML) and Kalman filter (KF) integrated localization system (KF-Loc). The ML model learns the complex wireless features for predicting the static position of the robot. When in dynamic motion, the static ML estimates suffer from position mispredictions, resulting in loss of accuracy. To overcome the loss in accuracy, we design and integrate a KF that learns the dynamics of the robot motion to provide highly accurate tracking. Our system achieves centimeter-level accuracy for the two aisles with RMSE of 0.35 m and 0.37 m, respectively. Further, compared with ML only localization systems, we achieve a significant reduction in RMSE by 28.5% and 54.3% within the two aisles.

Integrated Indoor Positioning System of Greenhouse Robot Based on UWB/IMU/ODOM/LIDAR.

Conventional mobile robots employ LIDAR for indoor global positioning and navigation, thus having strict requirements for the ground environment. Under the complicated ground conditions in the greenhouse, the accumulative error of odometer (ODOM) that arises from wheel slip is easy to occur during the long-time operation of the robot, which decreases the accuracy of robot positioning and mapping. To solve the above problem, an integrated positioning system based on UWB (ultra-wideband)/IMU (inertial measurement unit)/ODOM/LIDAR is proposed. First, UWB/IMU/ODOM is integrated by the Extended Kalman Filter (EKF) algorithm to obtain the estimated positioning information. Second, LIDAR is integrated with the established two-dimensional (2D) map by the Adaptive Monte Carlo Localization (AMCL) algorithm to achieve the global positioning of the robot. As indicated by the experiments, the integrated positioning system based on UWB/IMU/ODOM/LIDAR effectively reduced the positioning accumulative error of the robot in the greenhouse environment. At the three moving speeds, including 0.3 m/s, 0.5 m/s, and 0.7 m/s, the maximum lateral error is lower than 0.1 m, and the maximum lateral root mean square error (RMSE) reaches 0.04 m. For global positioning, the RMSEs of the x-axis direction, the y-axis direction, and the overall positioning are estimated as 0.092, 0.069, and 0.079 m, respectively, and the average positioning time of the system is obtained as 72.1 ms. This was sufficient for robot operation in greenhouse situations that need precise positioning and navigation.

Seamless Navigation, 3D Reconstruction, Thermographic and Semantic Mapping for Building Inspection.

We present a workflow for seamless real-time navigation and 3D thermal mapping in combined indoor and outdoor environments in a global reference frame. The automated workflow and partly real-time capabilities are of special interest for inspection tasks and also for other time-critical applications. We use a hand-held integrated positioning system (IPS), which is a real-time capable visual-aided inertial navigation technology, and augment it with an additional passive thermal infrared camera and global referencing capabilities. The global reference is realized through surveyed optical markers (AprilTags). Due to the sensor data’s fusion of the stereo camera and the thermal images, the resulting georeferenced 3D point cloud is enriched with thermal intensity values. A challenging calibration approach is used to geometrically calibrate and pixel-co-register the trifocal camera system. By fusing the terrestrial dataset with additional geographic information from an unmanned aerial vehicle, we gain a complete building hull point cloud and automatically reconstruct a semantic 3D model. A single-family house with surroundings in the village of Morschenich near the city of Jülich (German federal state North Rhine-Westphalia) was used as a test site to demonstrate our workflow. The presented work is a step towards automated building information modeling.

Robust Underwater Localization Using Acoustic Image Alignment for Autonomous Intervention Systems

Acoustic imaging sonar can be used as important navigation sensors for underwater intervention systems as they consistently provide spatial information about the surrounding environment, even in limited visibility conditions. However, acoustic imaging sonars are known for high spatial ambiguity and low resolution of their measurements, which makes it challenging to obtain precise navigational information. This paper presents a novel localization method based on sensor fusion using an integrated IMU-DVL system and an acoustic imaging sonar. A sonar simulator is implemented and used to estimate the pose of the robot based on a single acoustic image, and it is formulated as an image alignment problem between a simulated acoustic image and an actual acoustic image. For this, an approximate nearest neighbor search method is employed for initial pose estimation, and a newly developed acoustic image alignment method is applied to obtain more accurate results in a continuous pose space. These methods are then combined to construct an integrated localization system using all the navigation sensors on the robot. Its feasibility and utility of the proposed approach is shown through an experimental validation in a test tank.

Indoor and Outdoor Seamless Positioning Method Using UWB Enhanced Multi-Sensor Tightly-Coupled Integration

As one of the main methods in positioning and navigation, the Global Navigation Satellite System (GNSS) is widely applied in many areas, such as road engineering, automobile navigation and many other outdoor applications. In addition to an outdoor location-based service, there are many needs for indoor positioning. However, GNSS is hard to position indoors due to signal blockage, and outdoor–indoor transition areas where multipath effects are unavoidable and signal interference is severe. This article proposes a GNSS/INS/UWB tightly-coupled integration positioning methodology to address the problem of accurate positioning in GNSS-difficult areas and aims to provide seamless positioning in different scenarios. In indoor environments, the system uses the UWB/INS tightly-coupled integration positioning mode and periodically corrects the INS errors with range measurements obtained from the UWB transmitting unit to achieve centimeter-level accuracy. In outdoor GNSS-difficult areas like outdoor–indoor transition areas, a GNSS/INS/UWB tightly-coupled integration positioning system is proposed to ensure positioning accuracy continuity and further improve the reliability of the system. To evaluate the positioning performance of the proposed UWB enhanced multi-sensor tightly-coupled integration system, an experiment was conducted in a high-rise building. The experiment results show that the proposed integrated positioning system can provide seamless and accurate positioning results in both indoor and outdoor–indoor transition areas, with DRMS of 5.25 cm and RMSE of 10.18 cm.

Bionic Integrated Positioning Mechanism Based on Bioinspired Polarization Compass and Inertial Navigation System.

In this paper, to address the problem of positioning accumulative errors of the inertial navigation system (INS), a bionic autonomous positioning mechanism integrating INS with a bioinspired polarization compass is proposed. In addition, the bioinspired positioning system hardware and the integration model are also presented. Concerned with the technical issue of the accuracy and environmental adaptability of the integrated positioning system, the sun elevation calculating method based on the degree of polarization (DoP) and direction of polarization (E-vector) is presented. Moreover, to compensate for the latitude and longitude errors of INS, the bioinspired positioning system model combining the polarization compass and INS is established. Finally, the positioning performance of the proposed bioinspired positioning system model was validated via outdoor experiments. The results indicate that the proposed system can compensate for the position errors of INS with satisfactory performance.

Integrated Positioning System of Unmanned Automatic Vehicle in Coal Mines

Automatic positioning technology of underground vehicles has become the key technology for the intelligent development of coal mines. To improve the comprehensive positioning accuracy of unmanned automatic vehicles in coal mines, an 18-D model of an odometer-aided inertial navigation system (INS) and a positioning model of an extended Kalman filter-based ultra-wideband (UWB) are established based on the vehicle kinematics equation. A tight integrated state estimation model is proposed to restrain the long-time drift of the INS and enhance the instability of the UWB system. A local positioning reference system based on an infrared motion capture system is established for the first time. Experimental tests show that the average positioning error of the proposed algorithm is 6.03 cm in the forward direction, and the root-mean-square error increased by 36.09% and 38.24% compared with those of the INS and UWB system, respectively. It is experimentally verified that the integrated positioning system achieves decimeter-level positioning accuracy in a coal mine roadway environment.

Improving Tightly LiDAR/Compass/Encoder-Integrated Mobile Robot Localization with Uncertain Sampling Period Utilizing EFIR Filter

In order to overcome the uncertainty of the data sampling period of the sensor due to equipment reasons, a mobile robot localization system is developed under the uncertain sampling period for the tightly-fused light detection and ranging (LiDAR), compass, and encoder data. The errors of position and velocity, the robot’s yaw, and the sampling period are chosen as state variables. The ranges between the corner feature points (CFPs) and the mobile robot measured by the LiDAR, compass, and encoder are considered as an observation. Based on the tightly-integrated nonlinear model, the extended unbiased finite-impulse response (EFIR) filter fuses the sensors’ data for the integrated localization system. The performances of the traditional loosely-coupled integration scheme, tightly-coupled integration scheme with a constant sampling interval, and tightly-coupled integration with an uncertain sampling interval are compared based on real data. It is shown experimentally that the proposed scheme is more accurate then the traditional loosely-coupled integration and the one relying on a constant sampling interval, which improves by about 10.2%.

Robust inertial navigation system/ultra wide band integrated indoor quadrotor localization employing adaptive interacting multiple model-unbiased finite impulse response/Kalman filter estimator

A new adaptive interacting multiple model (IMM)-based unbiased finite impulse response (UFIR)/Kalman filter (KF) algorithm is proposed to provide accurate and robust mini quadrotor position information in indoor environments. The IMM-UFIR/KF algorithm is designed based on an integrated localization system, which combines recent data obtained from the inertial navigation system (INS) and ultra wide band (UWB) unit. In this scheme, adaptive UFIR filter and Kalman filters operate in parallel. The outputs of both filters are fused with probabilistic weights to correct the INS position, thereby reaching the best accuracy available from INS and UWB. It is shown experimentally that the output of the designed adaptive IMM-UFIR/KF-based localization system, which takes advantages of the KF accuracy and the UFIR filter robustness, ranges close to the most accurate estimator at each time instant.

Application of Low Cost Integrated Navigation System in Precision Agriculture

To improve the positioning accuracy of farming vehicle in precision agriculture, an integrated positioning system is proposed based on Global Navigation Satellite System (GNSS)/Strapdown Inertial Navigation System (SINS)/Wireless Sensor Networks (WSN) with low cost and high reliability. The principles of commonly used localization technologies in vehicle positioning are compared and the Received Signal Strength Indication (RSSI) based measurement method is chosen as the integrated positioning system for information fusion considering the complexity of the algorithm, positioning accuracy and hardware requirements in the application scenario. The research of wireless signal propagation loss model of farmland environment was conducted. A set of GNSS/SINS/WSN integrated positioning system is designed and implemented based on the requirement analysis of low cost, high reliability and precision. Simulation experiments were conducted and the results show that integrated positioning system can improve the position accuracy of agricultural machinery and meet the precision positioning requirements of agricultural machinery.

A SINS/DVL Integrated Positioning System through Filtering Gain Compensation Adaptive Filtering.

Because of the complex task environment, long working distance, and random drift of the gyro, the positioning error gradually diverges with time in the design of a strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) integrated positioning system. The use of velocity information in the DVL system cannot completely suppress the divergence of the SINS navigation error, which will result in low positioning accuracy and instability. To address this problem, this paper proposes a SINS/DVL integrated positioning system based on a filtering gain compensation adaptive filtering technology that considers the source of error in SINS and the mechanism that influences the positioning results. In the integrated positioning system, an organic combination of a filtering gain compensation adaptive filter and a filtering gain compensation strong tracking filter is explored to fuse position information to obtain higher accuracy and a more stable positioning result. Firstly, the system selects the indirect filtering method and uses the integrated positioning error to model the navigation parameters of the system. Then, a filtering gain compensation adaptive filtering method is developed by using the filtering gain compensation algorithm based on the error statistics of the positioning parameters. The positioning parameters of the system are filtered and information on errors in the navigation parameters is obtained. Finally, integrated with the positioning parameter error information, the positioning parameters of the system are solved, and high-precision positioning results are obtained to accurately position autonomous underwater vehicles (AUVs). The simulation results show that the SINS/DVL integrated positioning method, based on the filtering gain compensation adaptive filtering technology, can effectively enhance the positioning accuracy.

IMU/Magnetometer/Barometer/Mass-Flow Sensor Integrated Indoor Quadrotor UAV Localization with Robust Velocity Updates

Velocity updates have been proven to be important for constraining motion-sensor-based dead-reckoning (DR) solutions in indoor unmanned aerial vehicle (UAV) applications. The forward velocity from a mass flow sensor and the lateral and vertical non-holonomic constraints (NHC) can be utilized for three-dimensional (3D) velocity updates. However, it is observed that (a) the quadrotor UAV may have a vertical velocity trend when it is controlled to move horizontally; (b) the quadrotor may have a pitch angle when moving horizontally; and (c) the mass flow sensor may suffer from sensor errors, especially the scale factor error. Such phenomenons degrade the performance of velocity updates. Thus, this paper presents a multi-sensor integrated localization system that has more effective sensor interactions. Specifically, (a) the barometer data are utilized to detect height changes and thus determine the weight of vertical velocity update; (b) the pitch angle from the inertial measurement unit (IMU) and magnetometer data fusion is used to set the weight of forward velocity update; and (c) an extra mass flow sensor calibration module is introduced. Indoor flight tests have indicated the effectiveness of the proposed sensor interaction strategies in enhancing indoor quadrotor DR solutions, which can also be used for detecting outliers in external localization technologies such as ultrasonics.

A Stable SINS/UWB Integrated Positioning Method of Shearer Based on the Multi-Model Intelligent Switching Algorithm

The accurate stable measurement of position for a shearer is a key technology in realizing an automated fully mechanized coal mining face. Using as a base of SINS/UWB integrated positioning system, the signal interference and node damage of UWB often cause an invalid integrated positioning system, because of the bad working environment of the shearer in the coal mine. Consequently, the positioning accuracy of the SINS/UWB integrated system decreases quickly, due to the drift error of SINS. Aiming at two invalid styles include partial anchor node failure and all anchor nodes failure of UWB, this paper proposes a stable SINS/UWB integrated positioning system of a shearer using the multi-model intelligent switching method based on a tightly coupled integrated model and a decision tree fault-tolerant model. First of all, the tightly coupled integrated model and the decision tree fault-tolerant model are built using measured distance and the final position of UWB, respectively. Second, the multi-model intelligent switching algorithm can be established based on the shearer Markoff movement state and residual threshold judgment of a Kalman Filter. Finally, the experimental results show that the multi-model intelligent switching fault-tolerant positioning algorithm can effectively overcome increased positioning error of the tightly coupled model and the decision tree model based on these two invalid styles. The maximum position error with the positioning model proposed in this paper is 0.93 m in 327 s, and the positioning accuracy with a multi-model intelligent switching model is much greater than that with tightly coupled integration and the decision tree fault tolerant models.

A Fault-Tolerant Integrated Borehole Trajectory Location Method Based on Geomagnetism/IMU of MWD

In the oil industry, directional drilling technology plays a significant role in oil-gas exploration. The key to success of directional drilling technology is locate the borehole accurately using measurement while drilling instruments. Measurement While Drilling(MWD) technology has the problems of data loss and gross error in complex down hole environment. Aiming at the problems that gross error and data missed consequences the accuracy of positioning calculation seriously, this paper presents a method of fault-tolerant integrated borehole trajectory location based on geomagnetism/IMU of MWD system. Firstly, the geomagnetic survey system is established to locate the borehole position based on tri-axis fluxgate and tri-axis accelerometer. The Inertial Measurement Unit(IMU) calculates the borehole trajectory at the same time. Then, the fault-tolerant judgment mechanism is introduced to discriminate and evaluate data loss and gross error of measurement parameters. Furthermore, Kalman Filter algorithm is implied to construct fault-tolerant integrated borehole trajectory positioning system. Finally, the simulation experiment is performed on the drilling experimental platform. The experimental result shows that the fault-tolerant integrated borehole trajectory positioning system can detect data loss and gross error effectively. In addition, the location precision of the fault-tolerant integrated positioning system can be maintained within 1 meter when the time of data missed and gross error state lasts for 6min respectively. From the simulation experiments, comparing with the position calculation of pure geomagnetism, and the method of integrated MEMS and geomagnetism, the precision of the fault-tolerant integrated location calculation is improved by 68.7% and 62.8% respectively.

An improved INS/PDR/UWB integrated positioning method for indoor foot-mounted pedestrians

PurposeThe purpose of this paper is to relate to the real-time navigation and tracking of pedestrians in a closed environment. To restrain accumulated error of low-cost microelectromechanical system inertial navigation system and adapt to the real-time navigation of pedestrians at different speeds, the authors proposed an improved inertial navigation system (INS)/pedestrian dead reckoning (PDR)/ultra wideband (UWB) integrated positioning method for indoor foot-mounted pedestrians.Design/methodology/approachThis paper proposes a self-adaptive integrated positioning algorithm that can recognize multi-gait and realize a high accurate pedestrian multi-gait indoor positioning. First, the corresponding gait method is used to detect different gaits of pedestrians at different velocities; second, the INS/PDR/UWB integrated system is used to get the positioning information. Thus, the INS/UWB integrated system is used when the pedestrian moves at normal speed; the PDR/UWB integrated system is used when the pedestrian moves at rapid speed. Finally, the adaptive Kalman filter correction method is adopted to modify system errors and improve the positioning performance of integrated system.FindingsThe algorithm presented in this paper improves performance of indoor pedestrian integrated positioning system from three aspects: in the view of different pedestrian gaits at different speeds, the zero velocity detection and stride frequency detection are adopted on the integrated positioning system. Further, the accuracy of inertial positioning systems can be improved; the attitude fusion filter is used to obtain the optimal quaternion and improve the accuracy of INS positioning system and PDR positioning system; because of the errors of adaptive integrated positioning system, the adaptive filter is proposed to correct errors and improve integrated positioning accuracy and stability. The adaptive filtering algorithm can effectively restrain the divergence problem caused by outliers. Compared to the KF algorithm, AKF algorithm can better improve the fault tolerance and precision of integrated positioning system.Originality/valueThe INS/PDR/UWB integrated system is built to track pedestrian position and attitude. Finally, an adaptive Kalman filter is used to improve the accuracy and stability of integrated positioning system.

Integrity monitoring for Positioning of intelligent transport systems using integrated RTK‐GNSS, IMU and vehicle odometer

Reliable continuing positioning is a critical requirement for intelligent transportation systems (ITS). An integrated positioning system is presented, where the global navigation satellite systems (GNSS) real-time kinematic (RTK) method was mainly used. When RTK is not available, positioning was maintained by using Doppler measurements or by low-cost inertial measurement unit (IMU) coupled with vehicle odometer measurements. A new integrity monitoring (IM) method is presented that addresses each positioning mode of the proposed integrated system. Models for the protection levels (PLs) are presented to bound the position error (PE) along the direction of motion of the vehicle and for the cross-track direction. Both direction components are needed, for instance for collision avoidance and for lane identification. The method was assessed through a kinematic test performed in a dense urban environment. Results showed that by integrating GNSS RTK, Doppler with IMU + odometer, positioning was available all the time. For RTK, positioning accuracy was less than a decimetre and the IM availability was 99%, where the PLs bounded the PEs and were less than an alert limit of 1 m. Positioning using Doppler and IMU + odometer measurements bridged RTK breaks but at the sub-meter level accuracy when used for short periods.