GPS/INS Integrity in Airborne Mapping

The quality of the laser point cloud georeferencing in airborne laser scanning missions is largely related to the quality of the GPS solution. The latter is obtained by post- processing the differential carrier-phase measurements in order to reach the required accuracy. This implies that errors or unacceptable quality in the gathered data that cause problems for the ambiguity resolution in the post- processing step are detected much later. The objective of this thesis is to investigate new concepts of GPS data quality monitoring and to improve the GPS solution by using RAIM and WAAS/EGNOS integrity enhancement techniques. To do that, quality check algorithms based on indicators such as the signal-to-noise ratio, the cycle slip detection results or the phase tracking loop output are proposed and successfully tested. Furthermore, a new global quality check algorithm based on RAIM and cycle slip detection has been designed and tested with a focus on the chances to resolve correctly the ambiguities during the carrier-phase post-processing. The algorithms are implemented in a real- time quality check tool developed in a C/C++ environment whose performance shows that the provided quality indications enhance the GPS integrity by providing crucial information on the signal quality during the flight. This information enables problematic epoch identification and warns immediately the mission operator about problematic flightlines that should be flown again. This avoids final product quality degradation or expensive mission repetition. The thesis also presents the design of an RTK- GPS on-board solution via radio communication channel. The design has been tested during a flight and the results show that a sub-decimetric accuracy can be reached by this mean. The potential of using such a solution is high since this provides ultimate integrity test for phase data. Also, as the final laser point cloud has been georeferenced quite accurately using the real-time sensor observations and Kalman filtering, the economical gain of avoiding post- processing is substantial

Autarktic and Inertial Measurements based Low-cost Reconstruction of Motorcycle forward Speed

Although well established in the aviation community, low-cost and vehicle independent "black-box" technology for accident analysis, adapted to mass-market ground-based vehicles, is an emerging technology with growing importance. Whilst several produits suited for cars are available on the market, almost no devices adapted for motorcycles exist. Due mainly to their particular dynamics and lack of space for installing any external device, the design of a data-recorder technology for motorcycles is nontrival. This becomes even more challenging if the technology has to be independent of the motorcycle type, low-cost, easy and fast to mount, and not based on GNSS technology (for autonomy and privacy issues)

Implementation and Performance of a GPS/INS Tightly Coupled Assisted PLL Architecture Using MEMS Inertial Sensors

The use of global navigation satellite system receivers for navigation still presents many challenges in urban canyon and indoor environments, where satellite availability is typically reduced and received signals are attenuated. To improve the navigation performance in such environments, several enhancement methods can be implemented. For instance, external aid provided through coupling with other sensors has proven to contribute substantially to enhancing navigation performance and robustness. Within this context, coupling a very simple GPS receiver with an Inertial Navigation System (INS) based on low-cost micro-electro-mechanical systems (MEMS) inertial sensors is considered in this paper. In particular, we propose a GPS/INS Tightly Coupled Assisted PLL (TCAPLL) architecture, and present most of the associated challenges that need to be addressed when dealing with very-low-performance MEMS inertial sensors. In addition, we propose a data monitoring system in charge of checking the quality of the measurement flow in the architecture. The implementation of the TCAPLL is discussed in detail, and its performance under different scenarios is assessed. Finally, the architecture is evaluated through a test campaign using a vehicle that is driven in urban environments, with the purpose of highlighting the pros and cons of combining MEMS inertial sensors with GPS over GPS alone

Modeling and Processing Approaches for Integrated Inertial Navigation

The challenge of estimating the position, velocity and orientation in space in a precise and reliable way, at any time, with and without reception of satellite signals, is the core subject of this dissertation. To this end, the use of Bayesian filters which fuse outputs from autonomous inertial navigation with satellite positioning is a well-accepted and largely proven approach. The quality of integrated systems is mainly driven by the errors affecting the inertial sensors. This research intends to improve the navigation accuracy of INS/GNSS by proposing and investigating novel approaches at two levels. First, a new estimation framework is developed that allows to model complex composite stochastic processes. We consolidate the proposed estimator on a theoretical basis and validate it through simulations and experiments. Results show the ability of our method to estimate models for which other conventional approaches (e.g. Allan variance and likelihood-based estimators) fail, thereby supporting the challenging stage of navigation filter design. Second, we investigate filter designs accounting for inertial sensor redundancy at observation and state levels. The benefits brought by such filters in terms of navigation accuracy and adaptive modeling of sensor noise are discussed in the context of experiments. For that purpose, a redundant MEMS-based inertial navigation system was designed and operated on a vehicle. Compared to classical single-IMU based filters, we found a significant bounding of the position, velocity and attitude error when operating redundant inertial systems. Contrary to single-IMU/GNSS systems, the redundant configuration is able to self-evaluate the level of system noise and thus to catch the effects of the dynamics. The improved performance and robustness is attractive for many applications requiring reliable and accurate trajectory determination

In-flight Accuracy Estimation for Airborne Lidar Data

One of the main problems of today’s Airborne Laser Scanning (ALS) systems is the lack of reliable data quality assessment within or shortly after the airborne survey campaign. This paper presents the development and implementation of a tool that allows complete data quality assessment directly “on the fly”. Its prerequisite is the real-time (RT) GPS/INS processing and subsequent georeferencing of the laser returns. The core of this monitoring tool is a full ALS error propagation engine, yielding the estimation of the expected point cloud accuracy in-flight. The error propagation considers the errors due to the direct georeferencing (DG), the measurement errors of the laser itself (ranging accuracy, encoder errors, etc.) and the variation of the range-finder error due to changing scanning geometry. Unlike the first two error sources, which can be assessed by propagation of the functional relations, the influence of the scanning geometry is much harder to assess, as it requires a-priori knowledge of the local terrain normal and the footprint size. This paper presents the methodology to estimate these parameters directly from the laser point cloud and derive a final quality indicator reflecting the georeferencing quality and the scanning geometry. To predict the accuracy of the point cloud, the tool also features an algorithm predicting the likelihood of fixing the differential carrier-phase ambiguities in postprocessing. Further, the paper discusses the adopted strategy for data processing and communication in order to cope with the constraints imposed by RT processing. We validate the predicted data quality and accuracy estimates by first practical experiences

Theoretical limitations of allan variance-based regression for time series model estimation

This paper formally proves the statistical inconsistency of the Allan variance-based estimation of latent (composite) model parameters. This issue has not been sufficiently investigated and highlighted since it is a technique that is still being widely used in practice, especially within the engineering domain. Indeed, among others, this method is frequently used for inertial sensor calibration which often deals with latent time series models and practitioners in these domains are often unaware of its limitations. To prove the inconsistency of this method we firstly provide a formal definition and subsequently deliver its theoretical properties, highlighting its limitations by comparing it with another statistically sound method

Wavelet-based improvements for inertial sensor error modeling

The parametric estimation of stochastic error signals is a common task in many engineering applications such as inertial sensor calibration. In the latter case the error signals are often of complex nature and very few approaches are available to estimate the parameters of these processes. A frequently used approach for this purpose is the Maximum Likelihood (ML) which is usually implemented through a Kalman filter and found via the EM-algorithm. Although the ML is a statistically sound and efficient estimator, its numerical instability has brought to the use of alternative methods, the main one being the Generalized Method of Wavelet Moments (GMWM). The latter is a straightforward, consistent and computationally efficient approach which nevertheless loses statistical efficiency compared to the ML method. To narrow this gap, in this paper we show that the performance of the GMWM estimator can be enhanced by making use of model moments in addition to those provided by the vector of wavelet variances. The theoretical findings are supported by simulations that highlight how the new estimator not only improves the finite sample performance of the GMWM but also allows it to approach the statistical efficiency of the ML. Finally, a case study with an inertial sensor demonstrates how useful this development is for the purposes of sensor calibration