Adaptive covariance estimation method for LiDAR-Aided multi-sensor integrated navigation systems

The accurate estimation of measurements covariance is a fundamental problem in sensors fusion algorithms and is crucial for the proper operation of filtering algorithms. This paper provides an innovative solution for this problem and realizes the proposed solution on a 2D indoor navigation system for unmanned ground vehicles (UGVs) that fuses measurements from a MEMS-grade gyroscope, speed measurements and a light detection and ranging (LiDAR) sensor. A computationally efficient weighted line extraction method is introduced, where the LiDAR intensity measurements are used, such that the random range errors and systematic errors due to surface reflectivity in LiDAR measurements are considered. The vehicle pose change is obtained from LiDAR line feature matching, and the corresponding pose change covariance is also estimated by a weighted least squares-based technique. The estimated LiDAR-based pose changes are applied as periodic updates to the Inertial Navigation System (INS) in an innovative extended Kalman filter (EKF) design. Besides, the influences of the environment geometry layout and line estimation error are discussed. Real experiments in indoor environment are performed to evaluate the proposed algorithm. The results showed the great consistency between the LiDAR-estimated pose chan

Survey on Recent Advances in Integrated GNSSs Towards Seamless Navigation Using Multi-Sensor Fusion Technology

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.©2021 The Authors. Published by ION.fi=vertaisarvioimaton|en=nonPeerReviewed

Robust Multi-sensor Data Fusion for Practical Unmanned Surface Vehicles (USVs) Navigation

The development of practical Unmanned Surface Vehicles (USVs) are attracting increasing attention driven by their assorted military and commercial application potential. However, addressing the uncertainties presented in practical navigational sensor measurements of an USV in maritime environment remain the main challenge of the development. This research aims to develop a multi-sensor data fusion system to autonomously provide an USV reliable navigational information on its own positions and headings as well as to detect dynamic target ships in the surrounding environment in a holistic fashion. A multi-sensor data fusion algorithm based on Unscented Kalman Filter (UKF) has been developed to generate more accurate estimations of USV’s navigational data considering practical environmental disturbances. A novel covariance matching adaptive estimation algorithm has been proposed to deal with the issues caused by unknown and varying sensor noise in practice to improve system robustness. Certain measures have been designed to determine the system reliability numerically, to recover USV trajectory during short term sensor signal loss, and to autonomously detect and discard permanently malfunctioned sensors, and thereby enabling potential sensor faults tolerance. The performance of the algorithms have been assessed by carrying out theoretical simulations as well as using experimental data collected from a real-world USV projected collaborated with Plymouth University. To increase the degree of autonomy of USVs in perceiving surrounding environments, target detection and prediction algorithms using an Automatic Identification System (AIS) in conjunction with a marine radar have been proposed to provide full detections of multiple dynamic targets in a wider coverage range, remedying the narrow detection range and sensor uncertainties of the AIS. The detection algorithms have been validated in simulations using practical environments with water current effects. The performance of developed multi-senor data fusion system in providing reliable navigational data and perceiving surrounding environment for USV navigation have been comprehensively demonstrated

GA-SVR and pseudo-position-aided GPS/INS integration during GPS outage

The performance of Global Positioning System and Inertial Navigation System (GPS/INS) integrated navigation is reduced when GPS is blocked. This paper proposes an algorithm to overcome the condition where GPS is unavailable. Together with a parameter-optimised Genetic Algorithm (GA), a Support Vector Regression (SVR) algorithm is used to construct the mapping function between the specific force, angular rate increments of INS measurements and the increments of the GPS position. During GPS outages, the real-time pseudo-GPS position is predicted with the mapping function, and the corresponding covariance matrix is estimated by an improved adaptive filtering algorithm. A GPS/INS integration scheme is demonstrated where the vehicle travels along a straight line and around a curve, with respect to both low-speed-stable and high-speed-unstable navigation platforms. The results show that the proposed algorithm provides a better performance when GPS is unavailable

GNSS/Multi-Sensor Fusion Using Continuous-Time Factor Graph Optimization for Robust Localization

Accurate and robust vehicle localization in highly urbanized areas is challenging. Sensors are often corrupted in those complicated and large-scale environments. This paper introduces GNSS-FGO, an online and global trajectory estimator that fuses GNSS observations alongside multiple sensor measurements for robust vehicle localization. In GNSS-FGO, we fuse asynchronous sensor measurements into the graph with a continuous-time trajectory representation using Gaussian process regression. This enables querying states at arbitrary timestamps so that sensor observations are fused without requiring strict state and measurement synchronization. Thus, the proposed method presents a generalized factor graph for multi-sensor fusion. To evaluate and study different GNSS fusion strategies, we fuse GNSS measurements in loose and tight coupling with a speed sensor, IMU, and lidar-odometry. We employed datasets from measurement campaigns in Aachen, Duesseldorf, and Cologne in experimental studies and presented comprehensive discussions on sensor observations, smoother types, and hyperparameter tuning. Our results show that the proposed approach enables robust trajectory estimation in dense urban areas, where the classic multi-sensor fusion method fails due to sensor degradation. In a test sequence containing a 17km route through Aachen, the proposed method results in a mean 2D positioning error of 0.19m for loosely coupled GNSS fusion and 0.48m while fusing raw GNSS observations with lidar odometry in tight coupling.Comment: Revision of arXiv:2211.0540

Adaptive Indoor Pedestrian Tracking Using Foot-Mounted Miniature Inertial Sensor

This dissertation introduces a positioning system for measuring and tracking the momentary location of a pedestrian, regardless of the environmental variations. This report proposed a 6-DOF (degrees of freedom) foot-mounted miniature inertial sensor for indoor localization which has been tested with simulated and real-world data. To estimate the orientation, velocity and position of a pedestrian we describe and implement a Kalman filter (KF) based framework, a zero-velocity updates (ZUPTs) methodology, as well as, a zero-velocity (ZV) detection algorithm. The novel approach presented in this dissertation uses the interactive multiple model (IMM) filter in order to determine the exact state of pedestrian with changing dynamics. This work evaluates the performance of the proposed method in two different ways: At first a vehicle traveling in a straight line is simulated using commonly used kinematic motion models in the area of tracking (constant velocity (CV), constant acceleration (CA) and coordinated turn (CT) models) which demonstrates accurate state estimation of targets with changing dynamics is achieved through the use of multiple model filter models. We conclude by proposing an interactive multiple model estimator based adaptive indoor pedestrian tracking system for handling dynamic motion which can incorporate different motion types (walking, running, sprinting and ladder climbing) whose threshold is determined individually and IMM adjusts itself adaptively to correct the change in motion models. Results indicate that the overall IMM performance will at all times be similar to the best individual filter model within the IMM

GNSS/LiDAR-Based Navigation of an Aerial Robot in Sparse Forests

Autonomous navigation of unmanned vehicles in forests is a challenging task. In such environments, due to the canopies of the trees, information from Global Navigation Satellite Systems (GNSS) can be degraded or even unavailable. Also, because of the large number of obstacles, a previous detailed map of the environment is not practical. In this paper, we solve the complete navigation problem of an aerial robot in a sparse forest, where there is enough space for the flight and the GNSS signals can be sporadically detected. For localization, we propose a state estimator that merges information from GNSS, Attitude and Heading Reference Systems (AHRS), and odometry based on Light Detection and Ranging (LiDAR) sensors. In our LiDAR-based odometry solution, the trunks of the trees are used in a feature-based scan matching algorithm to estimate the relative movement of the vehicle. Our method employs a robust adaptive fusion algorithm based on the unscented Kalman filter. For motion control, we adopt a strategy that integrates a vector field, used to impose the main direction of the movement for the robot, with an optimal probabilistic planner, which is responsible for obstacle avoidance. Experiments with a quadrotor equipped with a planar LiDAR in an actual forest environment is used to illustrate the effectiveness of our approach