Ricerche di Geomatica 2011

Questo volume raccoglie gli articoli che hanno partecipato al Premio AUTeC 2011. Il premio è stato istituito nel 2005. Viene conferito ogni anno ad una tesi di Dottorato giudicata particolarmente significativa sui temi di pertinenza del SSD ICAR/06 (Topografia e Cartografia) nei diversi Dottorati attivi in Italia

Human and Robotic Mission to Small Bodies: Mapping, Planning and Exploration

This study investigates the requirements, performs a gap analysis and makes a set of recommendations for mapping products and exploration tools required to support operations and scientific discovery for near- term and future NASA missions to small bodies. The mapping products and their requirements are based on the analysis of current mission scenarios (rendezvous, docking, and sample return) and recommendations made by the NEA Users Team (NUT) in the framework of human exploration. The mapping products that sat- isfy operational, scienti c, and public outreach goals include topography, images, albedo, gravity, mass, density, subsurface radar, mineralogical and thermal maps. The gap analysis points to a need for incremental generation of mapping products from low (flyby) to high-resolution data needed for anchoring and docking, real-time spatial data processing for hazard avoidance and astronaut or robot localization in low gravity, high dynamic environments, and motivates a standard for coordinate reference systems capable of describing irregular body shapes. Another aspect investigated in this study is the set of requirements and the gap analysis for exploration tools that support visualization and simulation of operational conditions including soil interactions, environment dynamics, and communications coverage. Building robust, usable data sets and visualisation/simulation tools is the best way for mission designers and simulators to make correct decisions for future missions. In the near term, it is the most useful way to begin building capabilities for small body exploration without needing to commit to specific mission architectures

Vision Science and Technology at NASA: Results of a Workshop

A broad review is given of vision science and technology within NASA. The subject is defined and its applications in both NASA and the nation at large are noted. A survey of current NASA efforts is given, noting strengths and weaknesses of the NASA program

Robust vision based slope estimation and rocks detection for autonomous space landers

As future robotic surface exploration missions to other planets, moons and asteroids become more ambitious in their science goals, there is a rapidly growing need to significantly enhance the capabilities of entry, descent and landing technology such that landings can be carried out with pin-point accuracy at previously inaccessible sites of high scientific value. As a consequence of the extreme uncertainty in touch-down locations of current missions and the absence of any effective hazard detection and avoidance capabilities, mission designers must exercise extreme caution when selecting candidate landing sites. The entire landing uncertainty footprint must be placed completely within a region of relatively flat and hazard free terrain in order to minimise the risk of mission ending damage to the spacecraft at touchdown. Consequently, vast numbers of scientifically rich landing sites must be rejected in favour of safer alternatives that may not offer the same level of scientific opportunity. The majority of truly scientifically interesting locations on planetary surfaces are rarely found in such hazard free and easily accessible locations, and so goals have been set for a number of advanced capabilities of future entry, descent and landing technology. Key amongst these is the ability to reliably detect and safely avoid all mission critical surface hazards in the area surrounding a pre-selected landing location. This thesis investigates techniques for the use of a single camera system as the primary sensor in the preliminary development of a hazard detection system that is capable of supporting pin-point landing operations for next generation robotic planetary landing craft. The requirements for such a system have been stated as the ability to detect slopes greater than 5 degrees and surface objects greater than 30cm in diameter. The primary contribution in this thesis, aimed at achieving these goals, is the development of a feature-based,self-initialising, fully adaptive structure from motion (SFM) algorithm based on a robust square-root unscented Kalman filtering framework and the fusion of the resulting SFM scene structure estimates with a sophisticated shape from shading (SFS) algorithm that has the potential to produce very dense and highly accurate digital elevation models (DEMs) that possess sufficient resolution to achieve the sensing accuracy required by next generation landers. Such a system is capable of adapting to potential changes in the external noise environment that may result from intermittent and varying rocket motor thrust and/or sudden turbulence during descent, which may translate to variations in the vibrations experienced by the platform and introduce varying levels of motion blur that will affect the accuracy of image feature tracking algorithms. Accurate scene structure estimates have been obtained using this system from both real and synthetic descent imagery, allowing for the production of accurate DEMs. While some further work would be required in order to produce DEMs that possess the resolution and accuracy needed to determine slopes and the presence of small objects such as rocks at the levels of accuracy required, this thesis presents a very strong foundation upon which to build and goes a long way towards developing a highly robust and accurate solution

Analysis and Validation of a Vision-Based Pose Initialization Algorithm for Non-Cooperative Spacecrafts

The thesis addresses the issue of monocular relative pose determination for non-cooperative spacecrafts. An algorithm, called SVD method, is described and its peculiarities are discussed. The SVD technique is implemented both on a dataset of synthetic imagery of PRISMA mission's Tango spacecraft and on a dataset of real images of a CubeSat mock-up. The results are, then, analyzed so to highlight the fundamental limitations and the optimal operative range for the SVD architecture

Accurate, fast, and robust 3D city-scale reconstruction using wide area motion imagery

Multi-view stereopsis (MVS) is a core problem in computer vision, which takes a set of scene views together with known camera poses, then produces a geometric representation of the underlying 3D model Using 3D reconstruction one can determine any object's 3D profile, as well as knowing the 3D coordinate of any point on the profile. The 3D reconstruction of objects is a generally scientific problem and core technology of a wide variety of fields, such as Computer Aided Geometric Design (CAGD), computer graphics, computer animation, computer vision, medical imaging, computational science, virtual reality, digital media, etc. However, though MVS problems have been studied for decades, many challenges still exist in current state-of-the-art algorithms, for example, many algorithms still lack accuracy and completeness when tested on city-scale large datasets, most MVS algorithms available require a large amount of execution time and/or specialized hardware and software, which results in high cost, and etc... This dissertation work tries to address all the challenges we mentioned, and proposed multiple solutions. More specifically, this dissertation work proposed multiple novel MVS algorithms to automatically and accurately reconstruct the underlying 3D scenes. By proposing a novel volumetric voxel-based method, one of our algorithms achieved near real-time runtime speed, which does not require any special hardware or software, and can be deployed onto power-constrained embedded systems. By developing a new camera clustering module and a novel weighted voting-based surface likelihood estimation module, our algorithm is generalized to process di erent datasets, and achieved the best performance in terms of accuracy and completeness when compared with existing algorithms. This dissertation work also performs the very first quantitative evaluation in terms of precision, recall, and F-score using real-world LiDAR groundtruth data. Last but not least, this dissertation work proposes an automatic workflow, which can stitch multiple point cloud models with limited overlapping areas into one larger 3D model for better geographical coverage. All the results presented in this dissertation work have been evaluated in our wide area motion imagery (WAMI) dataset, and improved the state-of-the-art performances by a large margin.The generated results from this dissertation work have been successfully used in many aspects, including: city digitization, improving detection and tracking performances, real time dynamic shadow detection, 3D change detection, visibility map generating, VR environment, and visualization combined with other information, such as building footprint and roads.Includes bibliographical references