Photogrammetry | EPFL Graph Search

Photogrammetry is the science and technology of obtaining reliable information about physical objects and the environment through the process of recording, measuring and interpreting photographic images and patterns of electromagnetic radiant imagery and other phenomena. The term photogrammetry was coined by the Prussian architect Albrecht Meydenbauer, which appeared in his 1867 article "Die Photometrographie." There are many variants of photogrammetry. One example is the extraction of three-dimensional measurements from two-dimensional data (i.e. images); for example, the distance between two points that lie on a plane parallel to the photographic can be determined by measuring their distance on the image, if the scale of the image is known. Another is the extraction of accurate color ranges and values representing such quantities as albedo, specular reflection, metallicity, or ambient occlusion from photographs of materials for the purposes of physically based rendering. Close

Doppler-based positioning using LEO augmented satellite constellation - Simulations of perspectives

Alice Andreetti

The Global Navigation Satellite System (GNSS) refers to a constellation of satellites emitting signals from space, used to provide Position, Navigation and Timing (PNT) services to the receivers on Earth. Nowadays, the GNSS is exploited in a wide range of applications among which ground mapping, transportation, machine control, timing, surveying, defense, or aerial photogrammetry [1]. However, as we become more dependent on GNSS technology, we also become more vulnerable to its limitations [2]. The GNSS operates with satellites orbiting in Medium Earth Orbits (MEO, approximately 20200 [km] in altitude). On one hand, this great distance allows to have a large footprint of the satellite signal on Earth, but on the other hand, it requires the signal to cross a wide path in the atmosphere before reaching the receiver. This results in a decrease of signal strength, which is not an issue in open-sky condition, but becomes a limitation in deep attenuation environments, with little or no service in urban canyon, in dense cities or indoors [3]. In this contribution, a possible strategy to cope with this issue is proposed and evaluated through a simulation process. It implies the use of Low Earth Orbits (LEO) satellites as a support for the GNSS PNT service. Since nowadays no broadband big LEO constellation broadcasting navigation messages exist, in this project various LEO constellation setups have been simulated and tested. Because LEOs are much closer to Earth (200 to 1500 [km]), they move faster in the sky, passing overhead in minutes instead of hours like MEOs. This gives rise to the possibility to exploit the Doppler effect to accurately locate the user. Therefore, three different PNT algorithms, employing the Kalman filter, are implemented and the evaluation of the localization quality is made at four distinct locations on the globe. The results show that, for most of the cases, compared to the standard GPS pseudorange only PNT, the addition of LEO pseudoranges already improve the localization quality. The performance of the PNT algorithm increases even more with the exploitation of both pseudoranges and pseudorangerates (Doppler) measurements. Nevertheless, the choice of constellation parameters such as satellites altitude, total number of satellites, orbit inclination or the frequency of the emitted signal, has a great impact on the magnitude of the improvement in the localization quality. The factor that has the most significant influence seems to be the signal frequency and for that reason it must be carefully chosen: the higher the better. Moreover, it has been demonstrated that at high elevation cut-off angles (denied environments), the addition of LEO constellation is a valid option and allows us to obtain an accurate position even when the GNSS only can not provide a solution. Finally, the LEO only system seems to be a promising alternative to cope with GNSS jamming or spoofing. However, the optimal LEO parameters providing an accurate solution for all the location on the globe has not yet been identified. In conclusion, the result of this effort is the awareness that, once the optimal parameters for the LEO system have been found, it is a valid option to augment the GNSS, or even as a standalone backup, especially when the Doppler-based positioning is applied. Nevertheless, we are still far from that end and much more research needs to be done.

2020

A new method to determine multi-angular reflectance factor from lightweight multispectral cameras with sky sensor in a target-less workflow applicable to UAV

Manuel Simon Paul Cubero-Castan, Dai Shi, Christoph Strecha

A new physically based method to estimate hemispheric-directional reflectance factor (HDRF) from lightweight multispectral cameras that have a downwelling irradiance sensor is presented. It combines radiometry wilh photogrammetric computer vision to derive geometrically and radiometrically accurate data purely from the images, without requiring reflectance targets or any other additional information apart from the imagery. The sky sensor orientation is initially computed using photogrammetric computer vision and revised with a non linear regression comprising radiometric and photogrammetry-derived information. It works for both clear sky and overcast conditions. A ground-based test acquisition of a Spectralon target observed from different viewing directions and with different sun positions using a typical multispectral sensor configuration for clear sky and overcast showed that both the overall value and the directionality of the reflectance factor as reported in the literature were well retrieved. An RMSE of 3% for clear sky and up to 5 for overcast sky was observed.

ELSEVIER SCIENCE INC2019

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