Generation and Rendering of Interactive Ground Vegetation for Real-Time Testing and Validation of Computer Vision Algorithms

During the development process of new algorithms for computer vision applications, testing and evaluation in real outdoor environments is time-consuming and often difficult to realize. Thus, the use of artificial testing environments is a flexible and cost-efficient alternative. As a result, the development of new techniques for simulating natural, dynamic environments is essential for real-time virtual reality applications, which are commonly known as Virtual Testbeds. Since the first basic usage of Virtual Testbeds several years ago, the image quality of virtual environments has almost reached a level close to photorealism even in real-time due to new rendering approaches and increasing processing power of current graphics hardware. Because of that, Virtual Testbeds can recently be applied in application areas like computer vision, that strongly rely on realistic scene representations. The realistic rendering of natural outdoor scenes has become increasingly important in many application areas, but computer simulated scenes often differ considerably from real-world environments, especially regarding interactive ground vegetation. In this article, we introduce a novel ground vegetation rendering approach, that is capable of generating large scenes with realistic appearance and excellent performance. Our approach features wind animation, as well as object-to-grass interaction and delivers realistically appearing grass and shrubs at all distances and from all viewing angles. This greatly improves immersion, as well as acceptance, especially in virtual training applications. Nevertheless, the rendered results also fulfill important requirements for the computer vision aspect, like plausible geometry representation of the vegetation, as well as its consistence during the entire simulation. Feature detection and matching algorithms are applied to our approach in localization scenarios of mobile robots in natural outdoor environments. We will show how the quality of computer vision algorithms is influenced by highly detailed, dynamic environments, like observed in unstructured, real-world outdoor scenes with wind and object-to-vegetation interaction

Active learning of manipulation sequences

We describe a system allowing a robot to learn goal-directed manipulation sequences such as steps of an assembly task. Learning is based on a free mix of exploration and instruction by an external teacher, and may be active in the sense that the system tests actions to maximize learning progress and asks the teacher if needed. The main component is a symbolic planning engine that operates on learned rules, defined by actions and their pre- and postconditions. Learned by model-based reinforcement learning, rules are immediately available for planning. Thus, there are no distinct learning and application phases. We show how dynamic plans, replanned after every action if necessary, can be used for automatic execution of manipulation sequences, for monitoring of observed manipulation sequences, or a mix of the two, all while extending and refining the rule base on the fly. Quantitative results indicate fast convergence using few training examples, and highly effective teacher intervention at early stages of learning.Peer ReviewedPostprint (author’s final draft

Insights into Minor Group Rhinovirus Uncoating: The X-ray Structure of the HRV2 Empty Capsid

Upon attachment to their respective receptor, human rhinoviruses (HRVs) are internalized into the host cell via different pathways but undergo similar structural changes. This ultimately results in the delivery of the viral RNA into the cytoplasm for replication. To improve our understanding of the conformational modifications associated with the release of the viral genome, we have determined the X-ray structure at 3.0 Å resolution of the end-stage of HRV2 uncoating, the empty capsid. The structure shows important conformational changes in the capsid protomer. In particular, a hinge movement around the hydrophobic pocket of VP1 allows a coordinated shift of VP2 and VP3. This overall displacement forces a reorganization of the inter-protomer interfaces, resulting in a particle expansion and in the opening of new channels in the capsid core. These new breaches in the capsid, opening one at the base of the canyon and the second at the particle two-fold axes, might act as gates for the externalization of the VP1 N-terminus and the extrusion of the viral RNA, respectively. The structural comparison between native and empty HRV2 particles unveils a number of pH-sensitive amino acid residues, conserved in rhinoviruses, which participate in the structural rearrangements involved in the uncoating process

The “DLR Crash Report”: Towards a Standard Crash-Testing Protocol for Robot Safety - Part II: Discussions

After giving a rich data basis of our impact tests with standardized crash-test dummies in Part I of this work we address in Part II various aspects related to these tests in a case based discussion. The presented facts, the knowledge gained from our previous work, and the data from Part I lead us to recommendations for standardized crash-testing procedures in robotics. The proposed impact procedures will help to compare blunt robot-human impacts on a common basis. Furthermore, we will discuss further requirements which will enhance the completeness of testing procedures