Calibration Aspects of INS Navigation

The use of a Bayesian filter (e.g., Kalman filter) for the fusion of information from satellite positioning and inertial navigation is a common approach in many applications, where the knowledge of position, velocity, and attitude in space are of great interest. The correctness of these estimates depends on many factors, among others the quality of the sensor measurements and the errors within, which are directly reflected in the filter design. A calibration process allows compensating for deterministic influences (which in return improve for instance qualitatively the attitude initialization) and their inherent stochastic error signals required for filtering.

This thesis presents in the first part the development of methods to perform a thorough calibration of different sensors in-lab under controlled conditions and in-field for a simplified calibration with limited resources and equipment. The stochastic properties of error signals are analyzed in the second part. A novel approach called Generalized Method of Wavelet Moments (GMWM) allows investigating the error structure using wavelets, which is similar to the Allan variance. An intuitive online tool is presented, which grants simplified access to the GMWM framework that provides a consistent, identifiable, and computationally efficient estimation of stochastic model parameters. The parameters of these error models are then made dependent on an external covariate such as temperature or motion. Indeed, it is experimentally confirmed that these properties shape the stochastic behavior of the measurements and how the stochastic parameters relate functionally to the influence of the covariate. Later, such knowledge is included in the filter for the correct estimation of confidence levels.

The successful implementation of these proposed concepts is validated in a fully functional drone-system for mapping purposes. A real-time calibration scheme is applied first in-lab, later in-field to initialize the navigation processor. Apart from the benefit of achieving considerably better estimates of the attitude, and in case of satellite signal outage also of the position, the calibration allows for a simplified fusion of redundant inertial sensors. The improved performances through calibration and sensor redundancy are attractive to drone mapping applications relying on an accurate direct or integrated orientation such as lightweight airborne laser scanning systems or frame-cameras, which are utilized in the experiments.

Advanced Algorithmic and Architecture Designs for Future Satellite Navigation Receivers

Youssef Tawk

The use of global navigation satellite system (GNSS) receivers for navigation still presents many challenges, in particular in urban canyon and indoor environments where satellite availability is reduced and received signals are usually much atten- uated. In addition, the reception of additional signal replicas due to reflections on the surrounding environment, i.e. multipath, introduces biases in the pseudorange measurements, which in turn lead to extra positioning errors. The navigation per- formance of a GNSS receiver depends greatly on the behavior of the phase lock loop (PLL) and the delay lock loop (DLL). To maintain the robustness of these loops in such conditions, several enhancement methods can be implemented to improve upon standard stand-alone mass market receivers. For instance, well-known techniques include the use of multi-constellations to improve the availability of visible satellites, take advantage of the potential multipath mitigation of the new GNSS signals, and an increase of the integration time combined with a decrease of the PLL and DLL filters bandwidths to improve sensitivity. Moreover, external aiding in the form of time, Doppler, position, or almanac that can be provided through coupling with other sensors can also contribute substantially in improving navigation performance in challenging environments. The aim of this dissertation is to address the challenges of satellite based naviga- tion in demanding environments in order to improve the navigation performance of the future GNSS receivers. Within this context, two research directions are adopted in this thesis. The first is to explore the performance and advantages of the upcoming Galileo signals and in particular the E5 Alternate Binary Offset Carrier AltBOC(15,10), and the second is to investigate the potential of low-cost micro-electro-mechanical systems (MEMS) based inertial sensors to complement GNSS receivers. In the first research direction, we present investigations of the processing of Galileo E5ab in a full band or of only one of its components, i.e. either E5a or E5b. More specifically, a new acquisition algorithm is proposed for wiping off the secondary code and thereby increase the coherent integration time while requiring a reasonable implementation complexity as compared to other architectures. Moreover, an archi- tecture for tracking the E5 pilot channel as an AltBOC(15,10) or BPSK(10) modulation is introduced, and the performance of well-known discriminator types is analyzed using analytical derivations and simulations of linearity and stability regions, thermal noise tracking errors, multipath error envelopes and tracking thresholds. Different parameters, such as the front-end filter bandwidth, the early/late chip spacing, un- normalized and normalized discriminators, are taken into consideration. The results we obtain are used to illustrate the main advantages and drawbacks of using the E5 signal in demanding environments as well as to help defining the main tracking loop parameters for an enhanced performance. In the second research direction, we consider the coupling of a global positioning system (GPS) receiver with an inertial navigation system (INS) based on MEMS sen- sors. In the past, one of the main constraints holding back the proliferation of such hybrid systems was the price of the inertial sensors, but with the widespread dissemi- nation of MEMS-based sensors this is no longer the case. Therefore, a GPS/INS Tightly Coupled Assisted PLL (TCAPLL) architecture is proposed in this dissertation, and most of the associated challenges that need to be addressed when dealing with very- low-performance MEMS inertial sensors are presented. The architecture includes a data monitoring system responsible for checking the quality of the measurement flow to maintain robust tracking and accurate navigation. The implementation of the TCAPLL is discussed in detail, and its performance under different scenarios is assessed. Finally the proposed 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.

EPFL2013

GPS/INS integration for pedestrian navigation

This research has been sponsored by the Centre Suisse d'Electronique et de Microtechnique (CSEM) in Neuchâtel, Switzerland. It introduces a system and the algorithms for Pedestrian Navigation using a combination of sensors. The main objective is to localise a pedestrian anywhere and at any moment. The equipments utilised to fulfill this requirement are a Global Navigation Satellite System (GNSS) receiver and inertial sensors, which are attached to the person and as such need to be portable. An overview of Pedestrian Navigation constitutes the first part of the document. This new domain is examined from four different views: applications, tools (or sensors), architecture of the system and finally environment in which the pedestrian is travelling. As part of this process, the "state of the art" situation is presented. The approach followed in order to aid pedestrian to navigate is based on the Dead Reckoning technique coupled with GNSS. Consequently, the resolution of the travelled "distance" is separated from the resolution of the orientation of the walk. For the computation of the distance, a new technique based upon accelerometers and GNSS has been developed and demonstrated. The accelerometers are not used as a classical pedometer (counter of the number of steps) and the technique is not based on the double integration to obtain successively speed and distance. Instead, signal processing allows, considering individual parameters, the walking speed to be obtained directly from the signal of the accelerometers. This process, as well as the manner to determine the individual parameters, is presented in detail. The development of the algorithms is based on research performed in both the navigation and the medical domains (mainly in physiology). The computation of the orientation is more classical. It is based on the measurements made by a gyroscope and a GNSS receiver. The particularity of this study is the use of a single gyroscope to determine the orientation of the walk instead of three for the classical technique of inertial navigation. The influence of body movement on the gyroscope output has been deeply examined to determine the most appropriate way to process the signal of the gyroscope. The feasibility of the use of a single gyro, in the context of pedestrian navigation, is demonstrated. The potential added value for introducing a magnetic compass in the system is also evaluated. Finally a centralised Kalman filter has been designed and tested to merge all the sensors outputs, to combine the distance and the orientation, to integrate the Dead Reckoning solution and the GNSS solutions and to estimate all the parameters in a close to real-time process. The efficiency of this filter is demonstrated through different tests.

EPFL2003

Development of a robotic mobile mapping system by vision-aided inertial navigation

Fadi Atef Bayoud

Vision-based inertial-aided navigation is gaining ground due to its many potential applications. In previous decades, the integration of vision and inertial sensors was monopolised by the defence industry due to its complexity and unrealistic economic burden. After the technology advancement, high-quality hardware and computing power became reachable for the investigation and realisation of various applications. In this thesis, a mapping system by vision-aided inertial navigation was developed for areas where GNSS signals are unreachable, for example, indoors, tunnels, city canyons, forests, etc. In this framework, a methodology on the integration of vision and inertial sensors was presented, analysed and tested when the only available information at the beginning is a number of features with known location/coordinates (with no GNSS signals accessibility), thus employing the method of "SLAM: Simultaneous Localisation And Mapping". SLAM is a term used in the robotics community to describe the problem of mapping the environment and at the same time using this map to determine (or to help in determining) the location of the mapping device. In addition to this, a link between the robotics and geomatics community was established where briefly the similarities and differences were outlined in terms of handling the navigation and mapping problem. Albeit many differences, the goal is common: developing a "navigation and mapping system" that is not bounded to the limits imposed by the used sensors. Classically, terrestrial robotics SLAM is approached using LASER scanners to locate the robot relative to a structured environment and to map this environment at the same time. However, outdoors robotics SLAM is not feasible with LASER scanners alone due to the environment's roughness and absence of simple geometric features. Recently in the robotics community, the use of visual methods, integrated with inertial sensors, has gained an interest. These visual methods rely on one or more cameras (or video) and make use of a single Kalman Filter with a state vector containing the map and the robot coordinates. This concept introduces high non-linearity and complications to the filter, which then needs to run at high rates (more than 20 Hz) with simplified navigation and mapping models. In this study, SLAM is developed using the Geomatics Engineering approach. Two filters are used in parallel: the Least-Squares Adjustment (LSA) for feature coordinates determination and the Kalman Filter (KF) for navigation correction. For this, a mobile mapping system (independent of GPS) is introduced by employing two CCD cameras (one metre apart) and one IMU. Conceptually, the outputs of the LSA photogrammetric resection (position and orientation) are used as the external measurements for the inertial KF. The filtered position and orientation are subsequently employed in the Photogrammetric intersection to map the surrounding features that are used as control points for the resection in the next epoch. In this manner, the KF takes the form of a navigation only filter, with a state vector containing the corrections to the navigation parameters. This way, the mapping and localisation can be updated at low rates (1 to 2 Hz) and use more complete modelling. Results show that this method is feasible with limitation induced from the quality of the images and the number of used features. Although simulation showed that (depending on the image geometry) determining the features' coordinates with an accuracy of 5-10 cm for objects at distances of up to 10 metres is possible, in practice this is not achieved with the employed hardware and pixel measurement techniques. Navigational accuracies depend as well on the quality of the images and the number and accuracy of the points used in the resection. While more than 25 points are needed to achieve centimetre accuracy from resection, they have to be within a distance of 10 metres from the cameras; otherwise, the resulting resection output will be of insufficient accuracy and further integration quality deteriorates. The initial conditions highly affect SLAM performance; these are the method of IMU initialisation and the a-priori assumptions on error distribution. The geometry of the system will furthermore have a consequence on possible applications. To conclude, the development consisted in establishing a mathematical framework, as well as implementing methods and algorithms for a novel integration methodology between vision and inertial sensors. The implementation and validation of the software have presented the main challenges, and it can be considered the first of a kind where all components were developed from scratch, with no pre-existing modules. Finally, simulations and practical tests were carried out, from which initial conclusions and recommendations were drawn to build upon. It is the author's hope that this work will stimulate others to investigate further this interesting problem taking into account the conclusions and recommendations sketched herein.

EPFL2006