Generic Multisensor Integration Strategy and Innovative Error Analysis for Integrated Navigation

A modern multisensor integrated navigation system applied in most of civilian applications typically consists of GNSS (Global Navigation Satellite System) receivers, IMUs (Inertial Measurement Unit), and/or other sensors, e.g., odometers and cameras. With the increasing availabilities of low-cost sensors, more research and development activities aim to build a cost-effective system without sacrificing navigational performance. Three principal contributions of this dissertation are as follows: i) A multisensor kinematic positioning and navigation system built on Linux Operating System (OS) with Real Time Application Interface (RTAI), York University Multisensor Integrated System (YUMIS), was designed and realized to integrate GNSS receivers, IMUs, and cameras. YUMIS sets a good example of a low-cost yet high-performance multisensor inertial navigation system and lays the ground work in a practical and economic way for the personnel training in following academic researches. ii) A generic multisensor integration strategy (GMIS) was proposed, which features a) the core system model is developed upon the kinematics of a rigid body; b) all sensor measurements are taken as raw measurement in Kalman filter without differentiation. The essential competitive advantages of GMIS over the conventional error-state based strategies are: 1) the influences of the IMU measurement noises on the final navigation solutions are effectively mitigated because of the increased measurement redundancy upon the angular rate and acceleration of a rigid body; 2) The state and measurement vectors in the estimator with GMIS can be easily expanded to fuse multiple inertial sensors and all other types of measurements, e.g., delta positions; 3) one can directly perform error analysis upon both raw sensor data (measurement noise analysis) and virtual zero-mean process noise measurements (process noise analysis) through the corresponding measurement residuals of the individual measurements and the process noise measurements. iii) The a posteriori variance component estimation (VCE) was innovatively accomplished as an advanced analytical tool in the extended Kalman Filter employed by the GMIS, which makes possible the error analysis of the raw IMU measurements for the very first time, together with the individual independent components in the process noise vector

Benets of tight coupled architectures for the integration of GNSS receiver and Vanet transceiver

Vehicular adhoc networks (VANETs) are one emerging type of networks that will enable a broad range of applications such as public safety, traffic management, traveler information support and entertain ment. Whether wireless access may be asynchronous or synchronous (respectively as in the upcoming IEEE 8021.11p standard or in some alternative emerging solutions), a synchronization among nodes is required. Moreover, the information on position is needed to let vehicular services work and to correctly forward the messages. As a result, timing and positioning are a strong prerequisite of VANETs. Also the diffusion of enhanced GNSS Navigators paves the way to the integration between GNSS receivers and VANET transceiv ers. This position paper presents an analysis on potential benefits coming from a tightcoupling between the two: the dissertation is meant to show to what extent Intelligent Transportation System (ITS) services could benefit from the proposed architectur

Keyframe-based visual–inertial odometry using nonlinear optimization

Combining visual and inertial measurements has become popular in mobile robotics, since the two sensing modalities offer complementary characteristics that make them the ideal choice for accurate visual–inertial odometry or simultaneous localization and mapping (SLAM). While historically the problem has been addressed with filtering, advancements in visual estimation suggest that nonlinear optimization offers superior accuracy, while still tractable in complexity thanks to the sparsity of the underlying problem. Taking inspiration from these findings, we formulate a rigorously probabilistic cost function that combines reprojection errors of landmarks and inertial terms. The problem is kept tractable and thus ensuring real-time operation by limiting the optimization to a bounded window of keyframes through marginalization. Keyframes may be spaced in time by arbitrary intervals, while still related by linearized inertial terms. We present evaluation results on complementary datasets recorded with our custom-built stereo visual–inertial hardware that accurately synchronizes accelerometer and gyroscope measurements with imagery. A comparison of both a stereo and monocular version of our algorithm with and without online extrinsics estimation is shown with respect to ground truth. Furthermore, we compare the performance to an implementation of a state-of-the-art stochastic cloning sliding-window filter. This competitive reference implementation performs tightly coupled filtering-based visual–inertial odometry. While our approach declaredly demands more computation, we show its superior performance in terms of accuracy

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

Analysis of the Effect of Time Delay on the Integrated GNSS/INS Navigation Systems

The performance of tightly coupled GNSS/INS integration is known to be better than that of loosely coupled GNSS/INS integration. However, if the time synchronization error occurs between the GNSS receiver and INS(Inertial Navigation System), the situation reverses. The performance of loosely coupled GNSS/INS integration and tightly coupled GNSS/INS integration is analyzed and compared due to time synchronization error by computer simulation

ULTRALIGHT RADAR FOR SMALL AND MICRO-UAV NAVIGATION

This paper presents a radar approach to navigation of small and micro Unmanned Aerial Vehicles (UAV) in environments challenging for common sensors. A technique based on radar odometry is briefly explained and schemes for complete integration with other sensors are proposed. The focus of the paper is set on ultralight radars and interpretation of outputs of such sensor when dealing with autonomous navigation in complex scenario. The experimental setup used to analyse the proposed approach comprises one multi-rotor UAV and one ultralight commercial radar. Results from flight tests in which both forward-only motion and mixed motion are presented and analysed, providing a reference for understanding outputs of radar in complex scenarios. The radar odometry solution is compared with ground truth provided by GPS sensor

Integration of inertial navigation with global navigation satellite system

Táto práca sa zaoberá štúdiou inerciálnej navigácie, globálnym družicovým polohovým systémom a ich integráciou do jedného navigačného riešenia. V prvej časti práce je počítaný výstup inerciálnych rovníc na základe meraní z akcelerometrov a gyroskovpov. Tieto rovnice počítajú rotácie pomocov kvaterniónov a odstraňujú gravitáciu z meraní akcelerometrov. Ďalej sú rozoberané chyby inerciálnej meracej jednotky so zameraním na odstránenie offsetov. V teórii sú rozobraté rôzne metódy integrácii INS a GNSS. Kalmanov filter je použitý pre získanie výsledného navigačného riešenia, ktoré spája výhody oboch systémov. Výsledkom je natočenie, rýchlosť a poloha daného objektu.This paper deals with study of inertial navigation, global navigation satellite system, and their fusion into the one navigation solution. The first part of the work is to calculate the trajectory from accelerometers and gyroscopes measurements. Navigation equations calculate rotation with quaternions and remove gravity sensed by accelerometers. The equation’s output is in earth centred fixed navigation frame. Then, inertial navigation errors are discussed and focused to the bias correction. Theory about INS/GNSS inte- gration compares different integration architecture. The Kalman filter is used to obtain navigation solution for attitude, velocity and position with advantages of both systems.

A factorization approach to inertial affine structure from motion

We consider the problem of reconstructing a 3-D scene from a moving camera with high frame rate using the affine projection model. This problem is traditionally known as Affine Structure from Motion (Affine SfM), and can be solved using an elegant low-rank factorization formulation. In this paper, we assume that an accelerometer and gyro are rigidly mounted with the camera, so that synchronized linear acceleration and angular velocity measurements are available together with the image measurements. We extend the standard Affine SfM algorithm to integrate these measurements through the use of image derivatives