A dynamic two-dimensional (D2D) weight-based map-matching algorithm

Existing map-Matching (MM) algorithms primarily localize positioning fixes along the centerline of a road and have largely ignored road width as an input. Consequently, vehicle lane-level localization, which is essential for stringent Intelligent Transport System (ITS) applications, seems difficult to accomplish, especially with the positioning data from low-cost GPS sensors. This paper aims to address this limitation by developing a new dynamic two-dimensional (D2D) weight-based MM algorithm incorporating dynamic weight coefficients and road width. To enable vehicle lane-level localization, a road segment is virtually expressed as a matrix of homogeneous grids with reference to a road centerline. These grids are then used to map-match positioning fixes as opposed to matching on a road centerline as carried out in traditional MM algorithms. In this developed algorithm, vehicle location identification on a road segment is based on the total weight score which is a function of four different weights: (i) proximity, (ii) kinematic, (iii) turn-intent prediction, and (iv) connectivity. Different parameters representing network complexity and positioning quality are used to assign the relative importance to different weight scores by employing an adaptive regression method. To demonstrate the transferability of the developed algorithm, it was tested by using 5,830 GPS positioning points collected in Nottingham, UK and 7,414 GPS positioning points collected in Mumbai and Pune, India. The developed algorithm, using stand-alone GPS position fixes, identifies the correct links 96.1% (for the Nottingham data) and 98.4% (for the Mumbai-Pune data) of the time. In terms of the correct lane identification, the algorithm was found to provide the accurate matching for 84% (Nottingham) and 79% (Mumbai-Pune) of the fixes obtained by stand-alone GPS. Using the same methodology adopted in this study, the accuracy of the lane identification could further be enhanced if the localization data from additional sensors (e.g. gyroscope) are utilized. ITS industry and vehicle manufacturers can implement this D2D map-matching algorithm for liability critical and in-vehicle information systems and services such as advanced driver assistant systems (ADAS)

Enabling Robust State Estimation through Covariance Adaptation

Several robust state estimation frameworks have been proposed over the previous decades. Underpinning all of these robust frameworks is one dubious assumption. Specifically, the assumption that an accurate a priori measurement uncertainty model can be provided. As systems become more autonomous, this assumption becomes less valid (i.e., as systems start operating in novel environments, there is no guarantee that the assumed a priori measurement uncertainty model characterizes the sensors current observation uncertainty). In an attempt to relax this assumption, a novel robust state estimation framework is proposed. The proposed framework enables robust state estimation through the iterative adaptation of the measurement uncertainty model. The adaptation of the measurement uncertainty model is granted through non-parametric clustering of the estimator\u27 s residuals, which enables the characterization of the measurement uncertainty via a Gaussian mixture model. This Gaussian mixture model based measurement uncertainty characterization can be incorporated into any non-linear least square optimization routine. Within this dissertation, the proposed framework is instantiated into three novel robust state estimation algorithms: batch covariance estimation (BCE), batch covariance estimation over an augmented data space (BCE-AD), and incremental covariance estimation (ICE). To verify the proposed framework, three global navigation satellite system (GNSS) data sets were collected. The collected data sets provide varying levels of observation degradation to enable the characterization of the proposed algorithm on a diverse data set. Utilizing these data sets, it is shown that the proposed framework exhibits improved state estimation accuracy when compared to other robust estimation techniques when confronted with degraded data quality

Range filtering for sequential GPS receivers with external sensor augmentation

The filtering of the satellite range and range-rate measurements from single channel sequential Global Positioning System receivers is usually done with an extended Kalman filter which has state variables defined in terms of an orthogonal navigation reference frame. An attractive suboptimal alternative is range-domain filtering, in which the individual satellite measurements are filtered separately before they are combined for the navigation solution. The main advantages of range-domain filtering are decreased processing and storage requirements and simplified tuning. Several range filter mechanization alternatives are presented, along with an innovative approach for combining the filtered range-domain quantities to determine the navigation state estimate. In addition, a method is outlined for incorporating measurements from auxiliary sensors such as altimeters into the navigation state estimation scheme similarly to the satellite measurements. A method is also described for incorporating inertial measurements into the navigation state estimator as a process driver

Effective Target Aware Visual Navigation for UAVs

In this paper we propose an effective vision-based navigation method that allows a multirotor vehicle to simultaneously reach a desired goal pose in the environment while constantly facing a target object or landmark. Standard techniques such as Position-Based Visual Servoing (PBVS) and Image-Based Visual Servoing (IBVS) in some cases (e.g., while the multirotor is performing fast maneuvers) do not allow to constantly maintain the line of sight with a target of interest. Instead, we compute the optimal trajectory by solving a non-linear optimization problem that minimizes the target re-projection error while meeting the UAV's dynamic constraints. The desired trajectory is then tracked by means of a real-time Non-linear Model Predictive Controller (NMPC): this implicitly allows the multirotor to satisfy both the required constraints. We successfully evaluate the proposed approach in many real and simulated experiments, making an exhaustive comparison with a standard approach.Comment: Conference paper at "European Conference on Mobile Robotics" (ECMR) 201

Enabling Robust State Estimation through Measurement Error Covariance Adaptation

Accurate platform localization is an integral component of most robotic systems. As these robotic systems become more ubiquitous, it is necessary to develop robust state estimation algorithms that are able to withstand novel and non-cooperative environments. When dealing with novel and non-cooperative environments, little is known a priori about the measurement error uncertainty, thus, there is a requirement that the uncertainty models of the localization algorithm be adaptive. Within this paper, we propose the batch covariance estimation technique, which enables robust state estimation through the iterative adaptation of the measurement uncertainty model. The adaptation of the measurement uncertainty model is granted through non-parametric clustering of the residuals, which enables the characterization of the measurement uncertainty via a Gaussian mixture model. The provided Gaussian mixture model can be utilized within any non-linear least squares optimization algorithm by approximately characterizing each observation with the sufficient statistics of the assigned cluster (i.e., each observation's uncertainty model is updated based upon the assignment provided by the non-parametric clustering algorithm). The proposed algorithm is verified on several GNSS collected data sets, where it is shown that the proposed technique exhibits some advantages when compared to other robust estimation techniques when confronted with degraded data quality.Comment: 14 pages, 13 figures, Submitted to IEEE Transactions on Aerospace And Electronic System

Estimation of Multi-Constellation GNSS Observation Stochastic Properties Using a Single-Receiver Single-Satellite Data Validation Method

The single receiver single satellite validation method is a technique that screens data from each satellite independently to detect and identify faulty observations. A new method for estimation of the stochastic properties of multi-constellation GNSS observation is presented utilising parameters of this validation method. Agreement of the characteristics of the validation statistics with theory is used as the criterion to select the best precision of the observations, spectral density and correlation time of the unknowns. A curve fitting approach in an iterative scheme is employed. The method is applicable to any GNSS with any arbitrary number of frequencies. Demonstration of the method results and performance is given using multiple-frequency data from GPS, GLONASS and Galileo in static and kinematic modes

GNSS precise point positioning :the enhancement with GLONASS

PhD ThesisPrecise Point Positioning (PPP) provides GNSS navigation using a stand-alone receiver with no base station. As a technique PPP suffers from long convergence times and quality degradation during periods of poo satellite visibility or geometry. Many applications require reliable realtime centimetre level positioning with worldwide coverage, and a short initialisation time. To achieve these goals, this thesis considers the use of GLONASS in conjunction with GPS in kinematic PPP. This increases the number of satellites visible to the receiver, improving the geometry of the visible satellite constellation. To assess the impact of using GLONASS with PPP, it was necessary to build a real time mode PPP program. pppncl was constructed using a combination of Fortran and Python to be capable of processing GNSS observations with precise satellite ephemeris data in the standardised RINEX and SP3 formats respectively. pppncl was validated in GPS mode using both staticsites and kinematic datasets.In GPS only mode,one sigma accuracy of 6.4mm and 13mm in the horizontal and vertical respectively for 24h static positioning was seen. Kinematic horizontal and vertical accuracies of 21mm and 33mm were demonstrated. pppncl was extended to assess the impact of using GLONASS observations in addi- tion to GPS instatic and kinematic PPP. Using ESA and Veripos Apex G2 satel- lite orbit and clock products,the average time until 10cm 1D static accuracy was achieved, over arange of globally distributed sites, was seen to reduce by up to 47%. Kinematic positioning was tested for different modes of transport using real world datasets. GPS/GLONAS SPPP reduced the convergence time to decimetre accuracy by up to a factor of three. Positioning was seen to be more robust in comparison to GPS only PPP, primarily due to cycle slips not being present on both satellite systems on the occasions when they occurred,and the reduced impact of undetected outliersEngineering and Physical Sciences Research Council, Verip os/Subsea

Diagnostic Tools Using a Multi-Constellation Single-Receiver Single-Satellite Data Validation Method

The use of single-receiver single-satellite data validation parameters for numerical and graphical diagnostics of the multi-frequency observations is presented. This method validates Global Navigation Satellite System (GNSS) measurements of a single receiver where data from each satellite are independently processed using a geometry-free observation model with a reparameterised form of the unknowns. The method is applicable to any GNSS with any number of frequencies. The diagnostic tools are based on checking agreement of characteristics of the validation test statistics against theory. The use of these diagnostics in static and kinematic modes is demonstrated using multiple-frequency data from the three GNSS constellations; Global Positioning System (GPS), Globalnaya Navigatsionnaya Sputnikovaya Sistema (GLONASS) and Galileo