2,609 research outputs found
Information Aided Navigation: A Review
The performance of inertial navigation systems is largely dependent on the
stable flow of external measurements and information to guarantee continuous
filter updates and bind the inertial solution drift. Platforms in different
operational environments may be prevented at some point from receiving external
measurements, thus exposing their navigation solution to drift. Over the years,
a wide variety of works have been proposed to overcome this shortcoming, by
exploiting knowledge of the system current conditions and turning it into an
applicable source of information to update the navigation filter. This paper
aims to provide an extensive survey of information aided navigation, broadly
classified into direct, indirect, and model aiding. Each approach is described
by the notable works that implemented its concept, use cases, relevant state
updates, and their corresponding measurement models. By matching the
appropriate constraint to a given scenario, one will be able to improve the
navigation solution accuracy, compensate for the lost information, and uncover
certain internal states, that would otherwise remain unobservable.Comment: 8 figures, 3 table
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Tightly-Coupled Opportunistic Navigation for Deep Urban and Indoor Positioning
A strategy is presented for exploiting the frequency stability,
transmit location, and timing information of ambient radio-frequency โsignals of opportunityโ for the purpose of
navigating in deep urban and indoor environments. The
strategy, referred to as tightly-coupled opportunistic navigation
(TCON), involves a receiver continually searching
for signals from which to extract navigation and timing
information. The receiver begins by characterizing these
signals, whether downloading characterizations from a collaborative
online database or performing characterizations
on-the-fly. Signal observables are subsequently combined
within a central estimator to produce an optimal estimate
of position and time. A simple demonstration of the
TCON strategy focused on timing shows that a TCONenabled
receiver can characterize and use CDMA cellular
signals to correct its local clock variations, allowing it to
coherently integrate GNSS signals beyond 100 seconds.Aerospace Engineering and Engineering Mechanic
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
Performance Analysis of Constrained Loosely Coupled GPS/INS Integration Solutions
The paper investigates approaches for loosely coupled GPS/INS integration. Error performance is calculated using a reference trajectory. A performance improvement can be obtained by exploiting additional map information (for example, a road boundary). A constrained solution has been developed and its performance compared with an unconstrained one. The case of GPS outages is also investigated showing how a Kalman filter that operates on the last received GPS position and velocity measurements provides a performance benefit. Results are obtained by means of simulation studies and real dat
Coupled Inertial Navigation and Flush Air Data Sensing Algorithm for Atmosphere Estimation
This paper describes an algorithm for atmospheric state estimation that is based on a coupling between inertial navigation and flush air data sensing pressure measurements. In this approach, the full navigation state is used in the atmospheric estimation algorithm along with the pressure measurements and a model of the surface pressure distribution to directly estimate atmospheric winds and density using a nonlinear weighted least-squares algorithm. The approach uses a high fidelity model of atmosphere stored in table-look-up form, along with simplified models of that are propagated along the trajectory within the algorithm to provide prior estimates and covariances to aid the air data state solution. Thus, the method is essentially a reduced-order Kalman filter in which the inertial states are taken from the navigation solution and atmospheric states are estimated in the filter. The algorithm is applied to data from the Mars Science Laboratory entry, descent, and landing from August 2012. Reasonable estimates of the atmosphere and winds are produced by the algorithm. The observability of winds along the trajectory are examined using an index based on the discrete-time observability Gramian and the pressure measurement sensitivity matrix. The results indicate that bank reversals are responsible for adding information content to the system. The algorithm is then applied to the design of the pressure measurement system for the Mars 2020 mission. The pressure port layout is optimized to maximize the observability of atmospheric states along the trajectory. Linear covariance analysis is performed to assess estimator performance for a given pressure measurement uncertainty. The results indicate that the new tightly-coupled estimator can produce enhanced estimates of atmospheric states when compared with existing algorithms
Optimal Image-Aided Inertial Navigation
The utilization of cameras in integrated navigation systems is among the most recent scientific research and high-tech industry development. The research is motivated by the requirement of calibrating off-the-shelf cameras and the fusion of imaging and inertial sensors in poor GNSS environments. The three major contributions of this dissertation are
The development of a structureless camera auto-calibration and system calibration algorithm for a GNSS, IMU and stereo camera system. The auto-calibration bundle adjustment utilizes the scale restraint equation, which is free of object coordinates. The number of parameters to be estimated is significantly reduced in comparison with the ones in a self-calibrating bundle adjustment based on the collinearity equations. Therefore, the proposed method is computationally more efficient.
The development of a loosely-coupled visual odometry aided inertial navigation algorithm. The fusion of the two sensors is usually performed using a Kalman filter. The pose changes are pairwise time-correlated, i.e. the measurement noise vector at the current epoch is only correlated with the one from the previous epoch. Time-correlated errors are usually modelled by a shaping filter. The shaping filter developed in this dissertation uses Cholesky factors as coefficients derived from the variance and covariance matrices of the measurement noise vectors. Test results with showed that the proposed algorithm performs better than the existing ones and provides more realistic covariance estimates.
The development of a tightly-coupled stereo multi-frame aided inertial navigation algorithm for reducing position and orientation drifts. Usually, the image aiding based on the visual odometry uses the tracked features only from a pair of the consecutive image frames. The proposed method integrates the features tracked from multiple overlapped image frames for reducing the position and orientation drifts. The measurement equation is derived from SLAM measurement equation system where the landmark positions in SLAM are algebraically by time-differencing. However, the derived measurements are time-correlated. Through a sequential de-correlation, the Kalman filter measurement update can be performed sequentially and optimally. The main advantages of the proposed algorithm are the reduction of computational requirements when compared to SLAM and a seamless integration into an existing GNSS aided-IMU system
Depth-Camera-Aided Inertial Navigation Utilizing Directional Constraints.
This paper presents a practical yet effective solution for integrating an RGB-D camera and an inertial sensor to handle the depth dropouts that frequently happen in outdoor environments, due to the short detection range and sunlight interference. In depth drop conditions, only the partial 5-degrees-of-freedom pose information (attitude and position with an unknown scale) is available from the RGB-D sensor. To enable continuous fusion with the inertial solutions, the scale ambiguous position is cast into a directional constraint of the vehicle motion, which is, in essence, an epipolar constraint in multi-view geometry. Unlike other visual navigation approaches, this can effectively reduce the drift in the inertial solutions without delay or under small parallax motion. If a depth image is available, a window-based feature map is maintained to compute the RGB-D odometry, which is then fused with inertial outputs in an extended Kalman filter framework. Flight results from the indoor and outdoor environments, as well as public datasets, demonstrate the improved navigation performance of the proposed approach
๋ค์ค ์ผ์ ํญ๋ฒ์์คํ ์ ์ํ ์ฐํฉํ ๋ถ๋ณ ํ์ฅ์นผ๋งํํฐ
ํ์๋
ผ๋ฌธ(์์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ํญ๊ณต์ฐ์ฃผ๊ณตํ๊ณผ, 2022.2. ๋ฐ์ฐฌ๊ตญ.This thesis presents the federated invariant extended Kalman filter (IEKF) using multiple measurements. IEKF has superior estimation performance compared to EKF through the definition of state variables on matrix Lie group while using the framework of the EKF. The IEKF enables trajectory independent estimation when left- or right-invariant measurements are used with proper invariant error selection. As a result, the IEKF ensures the convergence and accuracy of estimation, even when the estimation error is large. Most IEKF studies assumed the use of single aiding measurement. However, navigation systems often use multiple aiding sensors to improve estimation performance in applications. When left- and right-invariant measurements are used simultaneously, implementing the LIEKF or RIEKF with a centralized filter structure causes some terms of the measurement matrix dependent on the current estimates, which results in IEKF losing its trajectory independent advantage. On the other hand, when a decentralized filter structure, especially a federated filter structure, is applied, the estimation becomes trajectory independent through separate update of each measurement in the local filters. This thesis proposes a fusion method of IEKF using the federated filter structure for simultaneous use of left- and right-invariant measurements. The performance of the proposed fusion method is validated through simulations. The error convergence and accuracy of the proposed method and the centralized IEKF are compared.๋ณธ ๋
ผ๋ฌธ์์๋ ๋ค์์ ๋ณด์ ์ผ์๋ฅผ ์ฌ์ฉํ๋ ํญ๋ฒ ์์คํ
์ ์ํ ์ฐํฉํ ๋ถ๋ณ ํ์ฅ ์นผ๋งํํฐ์ ๊ตฌํ์ ์ ์ํ๋ค. ๋ถ๋ณ ํ์ฅ ์นผ๋งํํฐ๋ ์ผ๋ฐ์ ์ธ ํ์ฅ ์นผ๋งํํฐ์ ํ๋ ์์ํฌ๋ ๊ทธ๋๋ก ์ฌ์ฉํ๋ฉด์ ์ํ๋ณ์๋ฅผ ํ๋ ฌ ๋ฆฌ ๊ทธ๋ฃน ์์์ ์ ์ํ์ฌ ํ์ฅ ์นผ๋งํํฐ ๋๋น ์ฐ์ํ ์ถ์ ์ฑ๋ฅ์ ๊ฐ์ง๋ค. ์ข๋ถ๋ณ ํน์ ์ฐ๋ถ๋ณ ์ธก์ ์น๋ฅผ ์ฌ์ฉํ ๋ ์ด์ ์ ํฉํ ๋ถ๋ณ ์ค์ฐจ ์ ์๋ฅผ ์ ํํ์ฌ ๊ตฌํํ๋ค๋ฉด ๊ถค์ ๋
๋ฆฝ์ ์ธ ์ถ์ ์ด ๊ฐ๋ฅํ๋ค. ๋๋ถ๋ถ์ ๋ถ๋ณ ํ์ฅ ์นผ๋งํํฐ์ ๋ํ ์ฐ๊ตฌ๋ค์ ๋จ์ผ ๋ณด์ ์ผ์์ ์ฌ์ฉ์ ๊ฐ์ ํ๋ค. ๊ทธ๋ฐ๋ฐ ์ค์ ์ ์ฉ์ ์์ด, ํญ๋ฒ ์์คํ
์ ์ถ์ ์ฑ๋ฅ์ ํฅ์ํ๊ธฐ ์ํด ๋ค์์ ๋ณด์ ์ผ์๋ฅผ ์ฌ์ฉํ๋ ๊ฒฝ์ฐ๊ฐ ๋ง๋ค. ์ข๋ถ๋ณ ์ธก์ ์น์ ์ฐ๋ถ๋ณ ์ธก์ ์น๊ฐ ๋ชจ๋ ์ฌ์ฉ๋๋ ์ํฉ์ด๋ผ๋ฉด, ์ค์์ง์คํ ์ข๋ถ๋ณ ํ์ฅ ์นผ๋งํํฐ์ ์ฐ๋ถ๋ณ ํ์ฅ ์นผ๋งํํฐ๋ ๋ชจ๋ ์ถ์ ์น์ ์ํฅ์ ๋ฐ๋ ์ธก์ ์น ํ๋ ฌ์ ์ฌ์ฉํ๊ฒ ๋๋ค. ์ด๋ก ์ธํด ๋ถ๋ณ ํ์ฅ์นผ๋งํํฐ๊ฐ ๊ฐ๋ ๊ฐ์ฅ ํฐ ์ฅ์ ์ธ ๊ถค์ ๋
๋ฆฝ ํน์ฑ์ ์๋๋ค. ๋ฐ๋ฉด์ ์ฐํฉํ ํํฐ ๊ตฌ์กฐ๋ฅผ ์ฌ์ฉํ๋ฉด ๊ฐ ์ธก์ ์น์ ํ ๋น๋ ๊ตญ์ ํํฐ์์ ์ ์ ํ ํํฐ๋ก ๊ฐ ์ธก์ ์น๋ฅผ ์ฒ๋ฆฌํ ์ ์๋ค. ๋ฐ๋ผ์ ์ด ๋
ผ๋ฌธ์์๋ ๋ถ๋ณ ํ์ฅ ์นผ๋งํํฐ์ ์ฐํฉํ ๊ตฌ์กฐ ๊ตฌํ์ ์ ์ํ๋ค. ๋ฆฌ ๊ทธ๋ฃน์ ์ฑ์ง์ ๊ณ ๋ คํ๋ ์ ์ ํ ์ตํฉ ๋ฐฉ์์ ์ฌ์ฉํ ๊ตฌ์กฐ๋ฅผ ์ ์ํ๋ฉฐ, ๊ทธ ์ฑ๋ฅ์ ์๋ฎฌ๋ ์ด์
์ ํตํด ํ์ธํ๋ค. ์ ์ํ ๋ฐฉ์๊ณผ ์ค์์ง์คํ ๋ถ๋ณ ํ์ฅ ์นผ๋งํํฐ๋ฅผ ์๋ ด์ฑ๊ณผ ์ถ์ ์ ํ๋์ ๊ด์ ์์ ๋น๊ตํ์๋ค.Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Objectives and contributions 3
Chapter 2 Related Works 5
2.1 Invariant extended Kalman filter (IEKF) 5
2.2 Federated filter 7
Chapter 3 Framework of invariant EKF 9
3.1 Mathematical preliminaries 9
3.2 States and model 10
3.2.1 Matrix Lie group states 10
3.2.2 Process model 12
3.2.3 Measurement model 15
3.2.4 Adjoint 16
3.3 IEKF for inertial navigation 17
3.3.1 IMU states and error states 17
3.3.2 Process model 20
3.3.3 Measurement model 22
3.3.4 Adjoint transformation 27
Chapter 4 IEKF Using Multiple Measurements 28
4.1 Centralized filter implementation 29
4.1.1 Centralized LIEKF 30
4.1.2 Centralized RIEKF 32
4.2 Federated filter implementation 34
4.2.1 Overall structure 34
4.2.2 Fusion process 39
4.3 Numerical simulations 40
4.3.1 Convergence test 43
4.3.2 Comparison of centralized IEKF and EKF 48
4.3.3 Comparison of IEKF and the proposed method 52
Chapter 5 Conclusion 60
5.1.1 Conclusion and summary 60
5.1.2 Future works 61
Bibliography 62
๊ตญ๋ฌธ์ด๋ก 68์
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