Hybridation MEMS/UWB pour la navigation pédestre intra-muros

Facing the expansion of geolocation needs, illustrated by the GALILEO European project, the growth of Location Based Services (LBS) and the need to identify the location of emergency mobile phone calls in Europe (standard E112), the research on localization techniques is booming. This thesis focuses on indoor pedestrian navigation and investigates a localization solution based on micro-electromechanical systems (MEMS) and ultra-wideband waves (UWB). MEMS based localization estimates the current location from a previously determined position using on-board low-cost inertial embedded sensors. Unfortunately, the performances of these autonomous systems are affected by large errors (typical of these sensors). In fact standalone solutions drift rapidly with time. Impulse-Radio UWB (IR-UWB) Times Of Arrival (TOA) are often used for localization purposes. This network based technology uses sensor networks, mainly attached to the infrastructure of the building to estimate the location of the transmitter with decimetre accuracy in ideal scenarii. However the indoor environment is hostile for radio propagation. Full of artificial obstacles, electromagnetic waves are disturbed and radiolocation performances are reduced. Construction materials also affect the magnetic field used to estimate the pedestrian's walking direction. In this context, the hybridization of these two complementary and uncorrelated technologies is promising. The study of the movement pattern of a pedestrian walking indoors induces two main outcomes on localization techniques. Firstly, random pedestrian movements complicate MEMS signal processing. Secondly, when the transmitter is worn by the user, for example around the neck, IR-UWB that propagates through the human body can hardly contribute to the localization. Optimal data fusion filters that hybridize a large set of observations : Angles Of Arrival (AOA), Time Differences Of Arrival (TDOA), accelerations, angular velocities and magnetic field measurements are presented. The coupling of UWB and MEMS data relies on an Extended Kalman Filter (EKF) complemented with specific procedures. Loose integration and tight integration are considered. Outlier detection processes within the radio data enrich the EKF. The most remarkable process is based on the RANSAC paradigm and employs the physical constraints of the pedestrian's walk described by biomechanics. In some cases, it enables the processing of reflected radio signals. A user equipped with a MEMS module and an UWB transceiver walked in the premises of the EPFL, following nine independent paths, for a total length of 380 m. The benefit of the MEMS/UWB hybridization filters are evaluated based on this experiment. The tight integration outperforms the loose coupling and enables indoor pedestrian localization with a one metre accuracy.

Optimal Data Fusion for Pedestrian Navigation based on UWB and MEMS

Bertrand Merminod

Indoor pedestrian navigation is probably a very challenging research area. In this context, an optimal data fusion filter that hybridises a large set of observations: angles of arrival (AOA), time differences of arrival (TDOA), accelerations, angular velocities and magnetic field measurements is presented. The coupling of UWB and MEMS data relies on an Extended Kalman Filter complemented with specific procedures. Geometry based algorithms and a RANSAC paradigm that mitigates the Non Line Of Sight (NLOS) UWB propagation are detailed. The benefit of the solution is evaluated and compared with the pure inertial positioning system.

2008

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

High Accuracy GNSS Based Navigation in GEO

Paul David Blunt, Cyril Botteron, Vincenzo Capuano, Pierre-André Farine, Endrit Shehaj

Although significant improvements in efficiency and performance of communication satellites have been achieved in the past decades, it is expected that the demand for new platforms in Geostationary Orbit (GEO) and for the On-Orbit Servicing (OOS) on the existing ones will continue to rise. Indeed, the GEO orbit is used for many applications including direct broadcast as well as communications. At the same time, Global Navigation Satellites System (GNSS), originally designed for land, maritime and air –applications, has been successfully used as navigation system in Low Earth Orbit (LEO) and its further utilization for navigation of geosynchronous satellites becomes a viable alternative offering many advantages over present ground based methods. Following our previous studies of GNSS signal characteristics in Medium Earth Orbit (MEO), GEO and beyond, in this research we specifically investigate the processing of different GNSS signals, with the goal to determine the best navigation performance they can provide in a GEO mission. Firstly, a detailed selection among different GNSS signals and different combinations of them is discussed, taking into consideration the L1 and L5 frequency bands, and the GPS and Galileo constellations. Then, the implementation of an Orbital Filter is summarized, which adaptively fuses the GNSS observations with an accurate orbital forces model. Finally, simulation tests of the navigation performance achievable by processing the selected combination of GNSS signals are carried out. The results obtained show an achievable positioning accuracy of less than one meter. In addition, hardware-in-the-loop tests are presented using a COTS receiver connected to our GNSS Spirent simulator, in order to collect real-time hardware-in-the-loop observations and process them by the proposed navigation module.