Semantic Visual Localization

Robust visual localization under a wide range of viewing conditions is a fundamental problem in computer vision. Handling the difficult cases of this problem is not only very challenging but also of high practical relevance, e.g., in the context of life-long localization for augmented reality or autonomous robots. In this paper, we propose a novel approach based on a joint 3D geometric and semantic understanding of the world, enabling it to succeed under conditions where previous approaches failed. Our method leverages a novel generative model for descriptor learning, trained on semantic scene completion as an auxiliary task. The resulting 3D descriptors are robust to missing observations by encoding high-level 3D geometric and semantic information. Experiments on several challenging large-scale localization datasets demonstrate reliable localization under extreme viewpoint, illumination, and geometry changes

The AREA Framework for Location-Based Smart Mobile Augmented Reality Applications

During the last years, the computational capabilities of smart mobile devices have been continuously improved by hardware vendors, raising new opportunities for mobile application engineers. Mobile augmented reality can be considered as one demanding scenario demonstrating that smart mobile applications are becoming more and more mature. In the AREA (Augmented Reality Engine Application) project, we developed a powerful kernel that enables location-based, mobile augmented reality applications. On top of this kernel, mobile application developers can realize sophisticated individual applications. The AREA kernel, in turn, allows for both robustness and high performance. In addition, it provides a flexible architecture that fosters the development of individual location-based mobile augmented reality applications. As a particular feature, the kernel allows for the handling of points of interests (POI) clusters. Altogether, advanced concepts are required to realize a location-based mobile augmented reality kernel that are presented in this paper. Furthermore, results of an experiment are presented in which the AREA kernel was compared to other location-based mobile augmented reality applications. To demonstrate the applicability of the kernel, we apply it in the context of various mobile applications. As a lesson learned, sophisticated mobile augmented reality applications can be efficiently run on present mobile operating systems and be effectively realized by engineers using the AREA framework. We consider mobile augmented reality as a killer application for mobile computational capabilities as well as the proper support of mobile users in everyday life

Advanced Algorithms for Location-Based Smart Mobile Augmented Reality Applications

During the last years, the computational capabilities of smart mobile devices have been continuously improved by hardware vendors, raising new opportunities for mobile application engineers. Mobile augmented reality is one scenario demonstrating that smart mobile applications are becoming increasingly mature. In the AREA (Augmented Reality Engine Application) project, we developed a kernel that enables such location-based mobile augmented reality applications. On top of the kernel, mobile application developers can easily realize their individual applications. The kernel, in turn, focuses on robustness and high performance. In addition, it provides a flexible architecture that fosters the development of individual location-based mobile augmented reality applications. In the first stage of the project, the LocationView concept was developed as the core for realizing the kernel algorithms. This LocationView concept has proven its usefulness in the context of various applications, running on iOS, Android, or Windows Phone. Due to the further evolution of computational capabilities on one hand and emerging demands of location-based mobile applications on the other, we developed a new kernel concept. In particular, the new kernel allows for handling points of interests (POI) clusters or enables the use of tracks. These changes required new concepts presented in this paper. To demonstrate the applicability of our kernel, we apply it in the context of various mobile applications. As a result, mobile augmented reality applications could be run on present mobile operating systems and be effectively realized by engineers utilizing our approach. We regard such applications as a good example for using mobile computational capabilities efficiently in order to support mobile users in everyday life more properly

Location-based Mobile Augmented Reality Applications: Challenges, Examples, Lessons Learned

The technical capabilities of modern smart mobile devices more and more enable us to run desktop-like applications with demanding resource requirements in mobile environments. Along this trend, numerous concepts, techniques, and prototypes have been introduced, focusing on basic implementation issues of mobile applications. However, only little work exists that deals with the design and implementation (i.e., the engineering) of advanced smart mobile applications and reports on the lessons learned in this context. In this paper, we give profound insights into the design and implementation of such an advanced mobile application, which enables location-based mobile augmented reality on two different mobile operating systems (i.e., iOS and Android). In particular, this kind of mobile application is characterized by high resource demands since various sensors must be queried at run time and numerous virtual objects may have to be drawn in realtime on the screen of the smart mobile device (i.e., a high frame count per second be caused). We focus on the efficient implementation of a robust mobile augmented reality engine, which provides location-based functionality, as well as the implementation of mobile business applications based on this engine. In the latter context, we also discuss the lessons learned when implementing mobile business applications with our mobile augmented reality engine

Enabling Tracks in Location-Based Smart Mobile Augmented Reality Applications

To assist users through contemporary mobile technology is demanded in a multitude of scenarios. Interestingly, more and more users crave for mobile assistance in their leisure time. Consequently, the number of mobile applications that support leisure activities increases significantly. Mobile augmented reality applications constitute an example for user assistance that is welcome in these scenarios. In the AREA (Augmented Reality Engine Application) project, we developed a kernel that enables sophisticated location-based mobile augmented reality applications. On top of this kernel, various projects were realized. In many of these projects, a feature to enable tracks was demanded. Tracks, for example, may assist users in the context of mountaineering. The development of an AREA algorithm that enables track handling requires new concepts that are presented in this paper. To demonstrate the performance of the developed algorithm, also results of an experiment are presented. As a lesson learned, mobile augmented reality applications that want to make use of the new algorithm can be efficiently run on present mobile operating systems and be effectively realized by engineers using the AREA framework. Altogether, the new track feature is another valuable step for AREA towards a comprehensive location-based mobile augmented reality framework

The European Fish Hazard Index – An assessment tool for screening hazard of hydropower plants for fish

Hydroelectricity is critical for decarbonizing global energy production, but hydropower plants affect rivers, disrupt their continuity, and threaten migrating fishes. This puts hydroelectricity production in conflict with efforts to protect threatened species and re-connect fragmented ecosystems. Assessing the impact of hydropower on fishes will support informed decision-making during planning, commissioning, and operation of hydropower facilities. Few methods estimate mortalities of single species passing through hydropower turbines, but no commonly agreed tool assesses hazards of hydropower plants for fish populations. The European Fish Hazard Index bridges this gap. This assessment tool for screening ecological risk considers constellation specific effects of plant design and operation, the sensitivity and mortality of fish species and overarching conservation and environmental development targets for a river. Further, it facilitates impact mitigation of new and existing hydropower plants of various types across Europe