Optimized Anchor Node Selection Algorithm Considering Power and Accuracy for Indoor Integrated Navigation System Based on INS/UWB

Affected by the spatial environment, the accuracy and stability of ultra-wideband (UWB) positioning in a narrow space are significantly lower than those in the general indoor environment, which limits navigation and positioning services in a complex scene. To improve the positioning accuracy and stability of a narrow space, this study proposed a positioning algorithm by combining Kalman filter (KF) and dilution of precision (DOP). Firstly, we calculated the DOP values of the target narrow space by changing the location of the test nodes throughout the space. Secondly, the initial coordinate values of the test nodes were calculated by the weighted least squares (WLS) positioning algorithm and were used as the observation values of KF. Finally, the DOP values were adaptively introduced into KF to update the coordinates of the nodes to be tested. The proposed algorithm was tested in two narrow scenes with different length–width ratios. The experimental results showed that the DOP values of the narrow space were much higher than that of the wide space. Furthermore, even if the ranging error was low, the positioning error was high in the narrow space. The proposed fusion positioning algorithm reported a higher positioning accuracy in the narrow space, and the higher DOP values of the scene, the greater the accuracy improvement of the algorithm. This study reveals that no matter how the base stations are configured, the DOP values of the narrow space are much higher than that of the wide space, thus causing larger positioning errors. The proposed positioning algorithm can effectively suppress the positioning error caused by the narrow spatial structure, so as to improve the positioning accuracy and stability.

Nowadays, unmanned aerial vehicles (UAVs) play a vital role in many applications and have been successfully employed in a wide variety of applications such as road surveying, precision agriculture, landslide monitoring, cultural heritage mapping, and pipeline monitoring. All of these applications require accurate and stable navigation system. Currently, most of the commercially available UAVs rely on the integration of the Global Satellite Navigation Systems (GNSS) and Inertial Navigation Systems (INS) to estimate the position, velocity, and attitude. The small form factor of modern UAVs allowed them to operate in more challenging environments such as urban and natural canyons. In these environments, the GNSS availability cannot be guaranteed, and hence the navigation solution will deteriorate dramatically during these GNSS signal outages due to the drift exhibited by the inertial navigation solution. Using other aiding sensors is crucial to limit the accumulated errors associated with INS measurements. Onboard cameras can offer a useful clue to support the navigation solution during the GNSS signal outage periods. Varieties of monocular visual odometry based on photogrammetric and Structure from Motion (SfM) approaches have been proposed to assist the navigation estimation process. The main problem of using the monocular VO technique is the loss of scale if neither external measurement nor a priori knowledge about the surrounding environment are available. Moreover, the estimated camera pose from VO is prone to drift with time. This paper introduces a novel approach for estimating the navigation states of an Unmanned Aerial Vehicle (UAV) by integrating the visual information that obtained from a monocular camera with the Inertial Measurement Unit (IMU) observations via an Extended Kalman Filter (EKF). Most of the current monocular VO algorithms rely on a calibrated camera model and apply the conventional photogrammetric and SfM approaches. While these approaches can help towards estimating the relative rotation and translation by tracking image features and applying geometrical constraints, they cannot estimate the motion scale using only the image visual features.

Tightly coupled integrated navigation system via factor graph for UAV indoor localization

As a fundamental prerequisite for a variety of location-based services, indoor location information has received increasing attention in recent years. Under the line-of-sight condition, the positioning accuracy of the indoor positioning technology based on ultrawideband (UWB) is acceptable for many applications, but under the non-line-of-sight condition, it degrades dramatically. The positioning accuracy can be significantly improved by the fusion of inertial measurement units and UWB sensors based on the extended Kalman filter (EKF) algorithm. However, when UWB measurements are affected by large non-Gaussian noise, the assumption of the EKF algorithm that observations are subject to Gaussian distribution for noise is invalid. Although the non-Gaussian noise can be handled by the robust EKF algorithm, this algorithm only uses the prior information to judge the reliability of the observations, and the positioning result is not stable when the number of beacons is small. To solve this problem, a method for successive updating of the covariance and posterior state of the observations in iterations based on an iterated extended Kalman filter (IEKF) is proposed. The marginal distribution of the posterior distribution is constructed and iteratively optimized, inhibiting the effect of non-Gaussian noise on UWB under a complex environment. The positioning results of the proposed method, the standard EKF algorithm, and the robust EKF algorithm, using different numbers of beacons, are compared. The results show that the positioning accuracy of the proposed algorithm is the highest under all scenarios. The proposed algorithm shows the smallest decrease in accuracy and presents the most stable positioning when the number of beacons is small, which is a common situation in practical applications.

This paper investigates into the feasibility of a combined use of Ultra-wideband (UWB) and Global Navigation Satellite System (GNSS) / Inertial Navigation System (INS) for integrated navigation in GNSS challenged environments. The performance of the original integrated GNSS/INS navigation system deteriorated rapidly due to the GNSS signal outages. The position information provided by UWB is used as the observation quantity of Extend Kalman Filter (EKF) to make up for the influence of GNSS signal loss. Considering the positioning service performance provided by UWB is closely related to the geometric distribution of base station of UWB, an optimization searching based on maximum convex-hull (OSMC) method is adopted to establish the optimal base station layout scheme. The features of navigation have been investigated in vehicle test with the results showing clear evidence that the method can resist the impact of GNSS outages effectively and the positioning information are stable and reliable.

The work conducted in this thesis is a part of an EU Horizon 2020 research initiative project known as DigiArt. This part of the DigiArt project presents and explores the design, formulation and implementation of probabilistically orientated state estimation algorithms with focus towards unmanned system positioning and three-dimensional (3D) mapping. State estimation algorithms are considered an influential aspect of any dynamic system with autonomous capabilities. Possessing the ability to predictively estimate future conditions enables effective decision making and anticipating any possible changes in the environment. Initial experimental procedures utilised a wireless ultra-wide band (UWB) based communication network. This system functioned through statically situated beacon nodes used to localise a dynamically operating node. The simultaneous deployment of this UWB network, an unmanned system and a Robotic Total Station (RTS) with active and remote tracking features enabled the characterisation of the range measurement errors associated with the UWB network. These range error metrics were then integrated into an Range based Extended Kalman Filter (R-EKF) state estimation algorithm with active outlier identification to outperform the native approach used by the UWB system for two-dimensional (2D) pose estimation.The study was then expanded to focus on state estimation in 3D, where a Six Degreeof-Freedom EKF (6DOF-EKF) was designed using Light Detection and Ranging (LiDAR) as its primary observation source. A two step method was proposed which extracted information between consecutive LiDAR scans. Firstly, motion estimation concerning Cartesian states x, y and the unmanned system’s heading (ψ) was achieved through a 2D feature matching process. Secondly, the extraction and alignment of ground planes from the LiDAR scan enabled motion extraction for Cartesian position z and attitude angles roll (θ) and pitch (φ). Results showed that the ground plane alignment failed when two scans were at 10.5◦ offset. Therefore, to overcome this limitation an Error State Kalman Filter (ES-KF) was formulated and deployed as a sub-system within the 6DOF-EKF. This enabled the successful tracking of roll, pitch and the calculation of z. The 6DOF-EKF was seen to outperform the R-EKF and the native UWB approach, as it was much more stable, produced less noise in its position estimations and provided 3D pose estimation.

A new integrated navigation system designed for small size Unmanned Aerial Vehicles (UAVs) is presented. The proposed system is based on a number of low-cost avionics sensors, including Global Navigation Satellite Systems (GNSS), Micro-Electro-Mechanical System (MEMS) based Inertial Measurement Unit (IMU) and Vision Based Sensors (VBS). The use of an Aircraft Dynamics Models (ADMs) to provide additional information to compensate for the shortcomings of Vision Based Navigation (VBN) and MEMS-IMU sensors in high-dynamics attitude determination tasks is also considered. Additionally, the research concentrates on the potential of carrier-phase GNSS for Attitude Determination (GAD) using interferometric techniques. The main objective is to design a compact, light and relatively inexpensive system capable of providing the required navigation performance (position and attitude data) in all phases of flight of small UAVs, with a special focus on precision approach and landing, where VBN techniques can be fully exploited in a multi-sensor data fusion architecture. An Extended Kalman Filter (EKF) is developed to integrate the information provided by the different sensors and to provide estimates of position, velocity and attitude of the UAV platform in real-time. Three different integrated navigation system architectures are implemented. The first architecture uses VBN at 20 Hz and GNSS at 1 Hz to augment the MEMS-IMU running at 100 Hz. The second mode also includes the ADM (computations performed at 100 Hz) to provide augmentation of the attitude channel. The third fusion architecture uses GNSS based attitude values. The simulations are carried out on the AEROSONDE UAV performing high-dynamics manoeuvres repre-sentative of the UAV operational flight envelope. Simulation of the VBN-IMU-GNSS (VIG) integrated navigation system shows that the system can attain position, velocity and attitude accuracies complying with Category Two (CAT II) precision approach requirements. Simulation of the VBN-IMU-GNSS-ADM (VIGA) system also shows promising results, since the achieved attitude accuracy is higher using the ADM-VBN-IMU than using VBN-IMU only. However, due to rapid divergence of the ADM virtual sensor, there is a need for frequent re-initialisation of the ADM data module, which is strongly dependent on the UAV flight dynamics and the specific manoeuvring transitions performed. In the simulation of the third integrated navigation system, the VIG system is augmented by employing the GAD, forming the VIG-GAD (VIGGA) system architecture. The performances achieved with the VIG, VIGA and VIGGA integrated Navigation and Guidance System (NGS) are presented and are in line with the International Civil Aviation Organization (ICAO) precision approach requirements.

Ultra wide band (UWB) sensors are widely used for indoor positioning; however, in many practical scenarios UWB signals are obscured by people, goods or other obstacles. This results in signal intensity attenuation, multipath effect and even signal loss, which causes a sharp decline in positioning accuracy. Fusion of pedestrian dead-reckoning (PDR) and UWB is an effective method to achieve high-accuracy positioning under non-line of sight conditions. While traditionally Bayesian filters, such as extended Kalman filter (EKF) and particle filter have been used for UWB/PDR fusion, recently Incremental Smoothing has been shown to achieve high accuracy in other application domains. In this paper, incremental Smoothing based on Tukey kernel function is proposed to fuse UWB and PDR data. We compare the performance of Incremental Smoothing with state of the art fusion algorithms based on EKF, and show that the incremental smoothing algorithm can achieve real-time positioning while exhibiting stronger robustness against intermittent noise, continuous noise and continuous interruption abnormality of UWB data.

During the past few decades, the presence of global navigation satellite systems (GNSSs) such as GPS, GLONASS, Beidou and Galileo has facilitated positioning, navigation and timing (PNT) for various outdoor applications. With the rapid increase in the number of orbiting satellites per GNSS, enhancements in the satellite-based augmentation systems (SBASs) such as EGNOS and WAAS, as well as commissioning new GNSS constellations, the PNT capabilities are maximized to reach new frontiers. Additionally, the recent developments in precise point positioning (PPP) and real time kinematic (RTK) algorithms have provided more feasibility to carrier-phase precision positioning solutions up to the third-dimensional localization. With the rapid growth of internet of things (IoT) applications, seamless navigation becomes very crucial for numerous PNT dependent applications especially in sensitive fields such as safety and industrial applications. Throughout the years, GNSSs have maintained sufficiently acceptable performance in PNT, in RTK and PPP applications however GNSS experienced major challenges in some complicated signal environments. In many scenarios, GNSS signal suffers deterioration due to multipath fading and attenuation in densely obscured environments that comprise stout obstructions. Recently, there has been a growing demand e.g. in the autonomous-things domain in adopting reliable systems that accurately estimate position, velocity and time (PVT) observables. Such demand in many applications also facilitates the retrieval of information about the six degrees of freedom (6-DOF - x, y, z, roll, pitch, and heading) movements of the target anchors. Numerous modern applications are regarded as beneficiaries of precise PNT solutions such as the unmanned aerial vehicles (UAV), the automatic guided vehicles (AGV) and the intelligent transportation system (ITS). Hence, multi-sensor fusion technology has become very vital in seamless navigation systems owing to its complementary capabilities to GNSSs. Fusion-based positioning in multi-sensor technology comprises the use of multiple sensors measurements for further refinement in addition to the primary GNSS, which results in high precision and less erroneous localization. Inertial navigation systems (INSs) and their inertial measurement units (IMUs) are the most commonly used technologies for augmenting GNSS in multi-sensor integrated systems. In this article, we survey the most recent literature on multi-sensor GNSS technology for seamless navigation. We provide an overall perspective for the advantages, the challenges and the recent developments of the fusion-based GNSS navigation realm as well as analyze the gap between scientific advances and commercial offerings. INS/GNSS and IMU/GNSS systems have proven to be very reliable in GNSS-denied environments where satellite signal degradation is at its peak, that is why both integrated systems are very abundant in the relevant literature. In addition, the light detection and ranging (LiDAR) systems are widely adopted in the literature for its capability to provide 6-DOF to mobile vehicles and autonomous robots. LiDARs are very accurate systems however they are not suitable for low-cost positioning due to the expensive initial costs. Moreover, several other techniques from the radio frequency (RF) spectrum are utilized as multi-sensor systems such as cellular networks, WiFi, ultra-wideband (UWB) and Bluetooth. The cellular-based systems are very suitable for outdoor navigation applications while WiFi-based, UWB-based and Bluetooth-based systems are efficient in indoor positioning systems (IPS). However, to achieve reliable PVT estimations in multi-sensor GNSS navigation, optimal algorithms should be developed to mitigate the estimation errors resulting from non-line-of-sight (NLOS) GNSS situations. Examples of the most commonly used algorithms for trilateration-based positioning are Kalman filters, weighted least square (WLS), particle filters (PF) and many other hybrid algorithms by mixing one or more algorithms together. In this paper, the reviewed articles under study and comparison are presented by highlighting their motivation, the methodology of implementation, the modelling utilized and the performed experiments. Then they are assessed with respect to the published results focusing on achieved accuracy, robustness and overall implementation cost-benefits as performance metrics. Our summarizing survey assesses the most promising, highly ranked and recent articles that comprise insights into the future of GNSS technology with multi-sensor fusion technique.

This paper exploits the idea of integrating the outputs of several optical flow sensors (OFSs) and a micro-electro-mechanical systems (MEMS) inertial measurement unit (IMU). Several optical flow sensors are fixed on an unmanned aerial vehicle (UAV). Their locations and orientations are different so as to detect the optical flow from different directions. By fusing the optical flow information and inertial navigation system (INS) measurements using an extended Kalman filter (EKF), the INS errors can be estimated and compensated. The integration of optical flow sensors and MEMS-based IMU provides a small size, low cost and self-contained scheme for UAVs navigation at low height above the ground in GPS denied environments. The preliminary simulation results presented in this article illustrate the effectiveness of the proposed integrated navigation scheme.

The demand for a reliable and accurate navigation system that can replace Global Positioning System (GPS) in GPS-denied environment has become increasingly imperative. For indoor environment where GPS is almost unavailable or unreliable, the utilization of other sensors such as inertial sensors becomes necessary. However, inertial sensors alone cannot sustain reliable long-term accuracy due to errors accumulation without external periodic corrections. Thus this paper proposes the utilization of Light Detection and Ranging (LiDAR) as an alternative system to provide periodic corrections. In this paper, a tightly-coupled integrated navigation system that integrates LiDAR, a single-axis gyroscope and wheel encoder is introduced. Straight lines detection and extraction algorithm is utilized to estimate the changes in orientation and range from LiDAR to the extracted line. LiDAR-estimated orientation change and range change to the extracted line feature between two consecutive LiDAR scans are first filtered out through a high rate extended Kalman Filter (EKF) to remove the effect of short-term noise associated with LiDAR scans. Then the smoothed orientation and range changes are fused by a low rate EKF with those predicted by gyroscope and wheel encoder. The proposed system is verified through real experiment on a wirelessly controlled Unmanned Ground Vehicle (UGV). Experimental results indicate that navigation accuracy has been improved to sub-meter and gyroscope bias is precisely estimated.

Positioning precision of navigation satellite system can be measured by two indicators: the dilution of precision (DOP) and the user equivalent range error (UERE). As the DOP values are only related to the spatial distribution of navigation satellites, the reduction of UERE is the main approach to improve the positioning precision. Equivalent satellite clock (ESC) has been used by Beidou satellite system (BDS) to reduce the UERE and to improve the user’s positioning accuracy. In this contribution, both the pseudo-range and carrier-phase measurements of BDS are used to compute the ESC respectively, and the corresponding navigation positioning performance are also compared. It is shown that the UERE improvement based on phase observables is 50.1%, while 32.1% based on pseudo-range observables. Kinematic positioning experiments of 4 MEGX stations are performed respectively under the standard PNT service and wide area differential service (WADS). It is shown that horizontal, vertical and three-dimensional positioning results of WADS are better than that of the standard PNT service.

The autonomous navigation system of feed-pushing robots is one of the key technologies for the intelligent breeding of dairy cows, and its accuracy has a significant influence on the quality of feed-pushing operations. Currently, the navigation methods of feed-pushing robots in the complex environment of cattle barns mainly include visual, LiDAR, and geomagnetic navigation, but there are still problems relating to low navigation accuracy. An autonomous navigation system based on ultra-wideband (UWB) positioning utilizing the dynamic forward-looking distance pure pursuit algorithm is proposed in this paper. First, six anchor nodes were arranged in the corners and central feeding aisle of a 30 × 86 m rectangular standard barn to form a rectangular positioning area. Then, utilizing the 9ITL-650 feed-pushing robot as a platform and integrating UWB wireless positioning technology, a global coordinate system for the cattle barn was established, and the expected path was planned. Finally, the pure pursuit model was improved based on the robot’s two-wheel differential kinematics model, and a dynamic forward-looking distance pure pursuit controller based on PID regulation was designed to construct a comprehensive autonomous navigation control system. Subsequently, field experiments were conducted in the cattle barn. The experimental results show that the static positioning accuracy of the UWB system for the feed-pushing robot was less than 16 cm under no-line-of-sight conditions in the cattle barn. At low speeds, the robot was subjected to linear tracking comparative experiments with forward-looking distances of 50, 100, 150, and 200 cm. The minimum upper-line distance of the dynamic forward-looking distance model was 205.43 cm. In the steady-state phase, the average lateral deviation was 3.31 cm, with an average standard deviation of 2.58 cm and the average root mean square error (RMSE) of 4.22 cm. Compared with the fixed forward-looking distance model, the average lateral deviation, the standard deviation, and the RMSE were reduced by 42.83%, 37.07%, and 42.90%, respectively. The autonomous navigation experiments conducted on the feed-pushing robot at travel speeds of 6, 8, and 10 m/min demonstrated that the maximum average lateral deviation was 7.58 cm, the maximum standard deviation was 8.22 cm, and the maximum RMSE was 11.07 cm, meeting the autonomous navigation requirements for feed-pushing operations in complex barn environments. This study provides support for achieving high-precision autonomous navigation control technology in complex environments.

Thanks to their flexibility and availability at reduced costs, Unmanned Aerial Vehicles (UAVs) have been recently used on a wide range of applications and conditions. Among these, they can play an important role in monitoring critical events (e.g., disaster monitoring) when the presence of humans close to the scene shall be avoided for safety reasons, in precision farming and surveying. Despite the very large number of possible applications, their usage is mainly limited by the availability of the Global Navigation Satellite System (GNSS) in the considered environment: indeed, GNSS is of fundamental importance in order to reduce positioning error derived by the drift of (low-cost) Micro-Electro-Mechanical Systems (MEMS) internal sensors. In order to make the usage of UAVs possible even in critical environments (when GNSS is not available or not reliable, e.g., close to mountains or in city centers, close to high buildings), this paper considers the use of a low cost Ultra Wide-Band (UWB) system as the positioning method. Furthermore, assuming the use of a calibrated camera, UWB positioning is exploited to achieve metric reconstruction on a local coordinate system. Once the georeferenced position of at least three points (e.g., positions of three UWB devices) is known, then georeferencing can be obtained, as well. The proposed approach is validated on a specific case study, the reconstruction of the façade of a university building. Average error on 90 check points distributed over the building façade, obtained by georeferencing by means of the georeferenced positions of four UWB devices at fixed positions, is 0.29 m. For comparison, the average error obtained by using four ground control points is 0.18 m.

Ultra-wideband (UWB) positioning performance is highly related to the accuracy of the coordinates of the fixed anchor nodes, which form the system infrastructure. The process of determining the position of the anchors is called calibration. In an anchor-based system, it is crucial for the fixed nodes to know their locations with the highest possible accuracy. However, in certain situations, it is almost impossible to perform the calibration manually, e.g., during emergency interventions. Moreover, calibration is always delicate and time-consuming. We designed an effortless and accurate self-calibration algorithm that does not require any manual intervention to precisely pinpoint the position of the anchors. This paper presents an innovative algorithm that combines machine learning and exploits the time resolution capabilities of UWB with adaptive physical settings to enable the automatic calibration of the fixed anchor nodes, even in realistic NLOS (non-line-of-sight) conditions. The self-calibration algorithm combines iterative gradient descent to pinpoint the positions of the anchors and uses error detection and correction from a convolutional neural network. Moreover, the algorithm can use a different set of settings for each anchor pair. This is done to ensure the most robust and accurate communication between nodes. Extensive measurements were carried out to allow anchors to estimate distances among each others. Distances were then combined and processed by the self-calibration algorithm. Experimental evaluation in two complex and large environments with many obstacles and reflections shows that accuracy reached by the algorithm is about 2.4 cm on average and 95th percentile is 5.7 cm, in best case. The results refer to the relative positions among the anchors. Results prove that in order to precisely calibrate the anchors nodes in an UWB positioning system, high correctness can be obtained by combining the accuracy of UWB together with deep learning and adaptive PHY modulation schemes.