1,721,016 research outputs found
Development of a robotic mobile mapping system by vision-aided inertial navigation : a geomatics approach
No full textVision-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.TOP
Capteurs et algorithmes pour la localisation autonome en mode pédestre
No full textThe challenge of knowing one's position in a precise and reliable way, at any time, with and without reception of satellite signals, represents an area fairly explored for the navigation of vehicles. To widen this service to the pedestrians requires a different approach that adapts to the dynamics, to the speed and especially to the total freedom of movement of the people. The traditional approach implements a triad of accelerometers and gyroscopes, which signals are integrated to obtain the relative displacement. This concept is unfortunately not judicious for a low-cost system. The principal reason is that the speed of displacement of a person is lost in the sensor noise level. In order to take into account all these specificities, an occurential approach was developed, based upon a subset of sensors as well as physiological and biomechanical parameters of the walk. This research is divided into three main directions. The first area of interest consists in the determination of the physiological parameters necessary to quantify the speed of walk and the step length. While the agitation of the accelerometer signals is a good speedometer, the frequency of the steps improves the robustness of the models. The influence of the gender added to the great human diversity imply the normalisation of the various relations deduced. Many tests carried out under conditions of everyday life reveal that the variation of the stride length, especially with the slope, strongly depends on the physical training of the person as well as on the duration of the climb or descent. Characteristic pattern were identified to differentiate between the forward, backward and lateral movements. The various suggested models were then favourably tested with some blind people, whose walking rhythm strongly varies according to the degree of confidence they have towards the course. The second part directly relates to the multiple technologies integrated to build an autonomous three-dimensional Pedestrian Navigation Module (PNM). The knowledge of the terrestrial magnetic field and its orientation makes it possible to determine the azimuth of displacement of a person. The use of a gyroscope improves the reliability of the system and facilitates the detection of magnetic disturbances. More stable in the short term than the compass, it is therefore the optimal complement under such circumstances. The altimetric information is obtained by barometric measurements which, according to the required precision, can be differential. The implementation of a GPS receiver allows the absolute positioning simultaneously to the calibration of the different sensor parameters and physiological models. The third part describes the integration of the models and measurements as well as the characteristics and treatments specific to pedestrian navigation. An initialisation phase is presented to individualize the parameters of the walk and adapt them from the general model. Hence, thanks to the compass-gyroscope integration together with the detection of any movement, this allows an optimal determination and filtering of the azimuth that has little or no temporal degradation. The consideration of several phenomena specific to the displacements of the humans brings artificial intelligence in pedestrian navigation. The coupling of the various sources of measurements, the influence of their precision on the computed position as well as their implication on the PNM reliability are described and illustrated. More than 550 km covered in various circumstances by 31 people allowed to validate the presented approach while fixing its limits.TOP
Mobile mapping en temps réel pour la saisie automatique d'axes routiers
No full textThe development of road telematics requires the management of ever-growing databases related to traffic fluidity, live consignment monitoring and vehicle fleet tracking, as to driver assistance. Such an effort relies on the tight synergy between navigation technology, telecommunication and geographic information, to enhance the maintenance and exploitation of the road network and, above all, to strengthen security. Consequently, an accurate knowledge of the road environment and topology is mandatory to implement applications of transport telematics. The early nineties experienced major advances in GPS/INS coupling and the market launch of affordable digital cameras. Thus, a considerable portion of road information is captured by vehicles equipped with such sensors, a technique known as "mobile mapping". The advantage of the kinematic collection of data – such as the pavement geometry, its surfacing quality and the positioning of road objects – lies in the much faster completion of the survey, hence an excellent cost effectiveness. However, the complexity of data georeferencing and the fusion of the results with video sequences require numerous hours of repetitive labor. Moreover, only the process completion reveals the correct recording of position measurements. Any further survey can only be decided a few days later. We propose to introduce the concept of "real time" in the field of mobile mapping. The determinist exploitation of the data captured during a kinematic survey aims at restricting human intervention in the sophisticated georeferencing process, while authorizing the dissemination of this technique outside well-informed communities. The other challenge of this thesis that lies in the automatic fusion of localization data with images, under tight time constraints. In these conditions, what are the tools and algorithms robust enough to ensure the quality control of the georeferencing of road objects? We intend to provide these concerns a pertinent answer, while demonstrating the validity of the concept via the automatic acquisition and interpretation of the road geometry.TOP
Cell-Based Deformation Monitoring via 3D Point Clouds
Full text linkDeformation is one of the most important phenomena in environmental science and engineering. Deformation of artificial and natural objects happens worldwide, such as structural deformation, landslide, subsidence, erosion, and rockfall. Monitoring and assessment of such deformation process is not only scientifically interesting, but also beneficial to hazard/risk control and prediction. In addition, it is also useful for regional planning and development. Deformation monitoring was driven by geodetic observations in the field of traditional geodetic surveying, based on the measurement of sparse points in a control network. Recently, with the rapid development of terrestrial LiDAR techniques, millions of points with associated three-dimensional coordinates (known as "3D point clouds") can be promptly captured in a few minutes. Compared to traditional surveying, terrestrial LiDAR offers great potential for deformation monitoring, because of various advantages such as fast data capture, high data density, and precise 3D object representation. By analysing 3D point clouds, the objective of this thesis is to provide an effective and efficient approach for deformation monitoring. Towards this goal, this thesis designs a new concept of "deformation map" for deformation representation and a novel "cell-based approach" for deformation computation. The main outcome of this thesis is a novel and rich approach that is able to automatically and incrementally compute a deformation map that enables a better understanding of structural and natural hazards with heterogeneous deformation characteristics. This work includes several dedicated contributions as follows. Hybrid Deformation Modelling. This thesis firstly provides a comprehensive investigation on the modelling requirements of various deformation phenomena. The requirements concern three main aspects, i.e., what has deformation (deformation object), which type of deformation, and how to describe deformation. Based on this detailed requirement analysis, we propose a rich and hybrid deformation model. This model is composed of meta-deformation, sub-deformation and deformation map, corresponding to deformation for a small cell, for a partial area, and for the whole object, respectively. Cell-based Deformation Computation. In order to automatically and incrementally extract heterogeneous deformation of the whole monitored object, we bring the "cell" concept into deformation monitoring. This thesis builds a cell-based deformation computing framework, which consists of three key steps: split, detect, and merge. Split is to divide the space of the object into many cells (uniform or irregular); detect is to extract the meta-deformation for individual cells by analysing the inside point clouds at two epochs; and merge is to group adjacent cells with similar deformation together and to form a consistent sub-deformation. As the final result, an informative deformation map is computed for describing the deformation for the whole object. Evaluation of Cell-based Approach. To evaluate such hybrid modelling and cell-based deformation computation, this thesis extensively studies both synthetic and real-life point cloud datasets: (1) by imitating a landslide scenario, we generate synthetic data using Matlab programming and practical settings, and compare the cell-based approach with traditional non-cell based geodetic methods; (2) by analysing two real-life cases of deformation in Switzerland, we further validate our approach and compare the results with third party sources (e.g., results provided by a surveying company, results computed by using a commercial software like 3DReshaper). Extension of Cell-based Approach. At the last stages of this thesis work, we particularly focus on providing several technical extensions to enhance this cell-based deformation monitoring approach. The main extensions include: (1) supporting dynamic cells instead of uniform cells when splitting the entire object space, (2) finding cell correspondence for the deformation scenarios that have large deformation like rockfalls, (3) movement tracking with data-driven cells which have irregular cell shape that can be automatically determined by the deformation boundary itself, (4) designing an adaptive modelling strategy that is able to accordingly select a suitable model for detecting meta-deformation of cells, and (5) computing deformation evolution for a monitored object with more than two epochs of point cloud datasets.TOP
Remote sensing of the nearshore zone using a rotary-wing UAV
No full textThe objective of this project is to develop a low-cost and flexible solution for mapping the nearshore bathymetry using a small quadcopter that hovers above the surf zone.SIE-
GPS/INS integration for pedestrian navigation
Full text linkThis 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.TOP
Suivi de la qualité des données spatiales au cours de leur acquisition et de leurs traitements
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Konzeption und Entwicklung neuer interaktiver, multimedialer Lern- und Arbeitsmethoden für die geodätische Ausgleichsrechnung
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