Monitoring and Adapting the Physical State of a Camera for Autonomous Vehicles

Autonomous vehicles and robots require increasingly more robustness and reliability to meet the demands of modern tasks. These requirements specially apply to cameras onboard such vehicles because they are the predominant sensors to acquire information about the environment and support actions. Cameras must maintain proper functionality and take automatic countermeasures if necessary. However, few works examine the practical use of a general condition monitoring approach for cameras and designs countermeasures in the context of an envisaged high-level application. We propose a generic and interpretable self-health-maintenance framework for cameras based on data- and physically-grounded models. To this end, we determine two reliable, real-time capable estimators for typical image effects of a camera in poor condition (blur, noise phenomena and most common combinations) by comparing traditional and retrained machine learning-based approaches in extensive experiments. Furthermore, we demonstrate on a real-world ground vehicle how one can adjust the camera parameters to achieve optimal whole-system capability based on experimental (non-linear and non-monotonic) input-output performance curves, using object detection, motion blur and sensor noise as examples. Our framework not only provides a practical ready-to-use solution to evaluate and maintain the health of cameras, but can also serve as a basis for extensions to tackle more sophisticated problems that combine additional data sources (e.g., sensor or environment parameters) empirically in order to attain fully reliable and robust machines

Multimodal retinal image registration using a fast principal component analysis hybrid-based similarity measure

Multimodal retinal images (RI) are extensively used for analysing various eye diseases and conditions such as myopia and diabetic retinopathy. The incorporation of either two or more RI modalities provides complementary structure information in the presence of non-uniform illumination and low-contrast homogeneous regions. It also presents significant challenges for retinal image registration (RIR). This paper investigates how the Expectation Maximization for Principal Component Analysis with Mutual Information (EMPCA-MI) algorithm can effectively achieve multimodal RIR. This iterative hybrid-based similarity measure combines spatial features with mutual information to provide enhanced registration without recourse to either segmentation or feature extraction. Experimental results for clinical multimodal RI datasets comprising colour fundus and scanning laser ophthalmoscope images confirm EMPCA-MI is able to consistently afford superior numerical and qualitative registration performance compared with existing RIR techniques, such as the bifurcation structures method

A camera self-health-maintenance system based on sensor artificial intelligence

Autonomous vehicles and robots require increasingly more robustness and reliability to meet the demands of modern tasks. These requirements specially apply to cameras onboard such vehicles because they are the predominant sensors to acquire information about the environment and support actions. However, there are versatile undesirable states cameras can encounter. Hence, cameras must maintain proper functionality and take automatic countermeasures if necessary. Currently, there is only little work that examines the practical use of a general condition monitoring approach for cameras and designs countermeasures in the context of an envisaged high-level application. We propose a self-health-maintenance framework for cameras with focus on blur and noise, based on artificial intelligence and the incorporation of additional physical knowledge of the sensor (Sensor AI)

Why finance professors should be teaching Nietzsche

<p><strong>Abstract:</strong> Retinal images (RI) are widely used to diagnose a variety of eye conditions and diseases such as myopia and diabetic retinopathy. They are inherently characterised by having nonuniform illumination and low-contrast homogeneous regions which represent a unique set of challenges for retinal image registration (RIR). This paper investigates using the expectation maximization for principal component analysis based mutual information (EMPCA-MI) algorithm in RIR. It combines spatial features with mutual information to efficiently achieve improved registration performance. Experimental results for mono-modal RI datasets verify that EMPCA-MI<br>together with Powell-Brent optimization affords superior robustness in comparison with existing RIR methods, including the geometrical features method.</p> <p><br><strong>Index Terms</strong>— Image registration, principal component analysis, mutual information, expectation-maximization algorithms, retinopathy.</p> <p> </p> <p><strong>Poster presented at</strong>: 38th International Conference on Acoustics, Speech, and Signal Processing<br>(ICASSP), 26th to 31st May 2013, Vancouver, Canada.<br>doi: 10.1109/ICASSP.2013.6637824</p

Sensor Artificial Intelligence and its Application to Space Systems - A White Paper

A white paper resulting from the 1st Workshop on Sensor AI, April 2020; organized by DLR and the ECDF.Information and communication technologies have accompanied our everyday life for years. A steadily increasing number of computers, cameras, mobile devices, etc. generate more and more data, but at the same time we realize that the data can only partially be analyzed with classical approaches. The research and development of methods based on artificial intelligence (AI) made enormous progress in the area of interpretability of data in recent years. With growing experience, both, the potential and limitations of these new technologies are increasingly better understood. Typically, AI approaches start with the data from which information and directions for action are derived. However, the circumstances under which such data are collected and how they change over time are rarely considered. A closer look at the sensors and their physical properties within AI approaches will lead to more robust and widely applicable algorithms. This holistic approach which considers entire signal chains from the origin to a data product, "Sensor AI", is a highly relevant topic with great potential. It will play a decisive role in autonomous driving as well as in areas of automated production, predictive maintenance or space research. The goal of this white paper is to establish "Sensor AI" as a dedicated research topic. We want to exchange knowledge on the current state-of-the-art on Sensor AI, to identify synergies among research groups and thus boost the collaboration in this key technology for science and industry

In situ science on Phobos with the Raman spectrometer for MMX (RAX): preliminary design and feasibility of Raman meausrements

Mineralogy is the key to understanding the origin of Phobos and its position in the evolution of the Solar System. In situ Raman spectroscopy on Phobos is an important tool to achieve the scientifc objectives of the Martian Moons eXploration (MMX) mission, and maximize the scientifc merit of the sample return by characterizing the mineral composition and heterogeneity of the surface of Phobos. Conducting in situ Raman spectroscopy in the harsh environment of Phobos requires a very sensitive, compact, lightweight, and robust instrument that can be carried by the compact MMX rover. In this context, the Raman spectrometer for MMX (i.e., RAX) is currently under development via international collaboration between teams from Japan, Germany, and Spain. To demonstrate the capability of a compact Raman system such as RAX, we built an instrument that reproduces the optical performance of the fight model using commercial of-the-shelf parts. Using this performance model, we measured mineral samples relevant to Phobos and Mars, such as anhydrous silicates, carbonates, and hydrous minerals. Our measurements indicate that such minerals can be accurately identifed using a RAX-like Raman spectrometer. We demonstrated a spectral resolution of approximately 10 cm−1, high enough to resolve the strongest olivine Raman bands at ~820 and ~850 cm−1, with highly sensitive Raman peak measurements (e.g., signal-to-noise ratios up to 100). These results strongly suggest that the RAX instrument will be capable of determining the minerals expected on the surface of Phobos, adding valuable information to address the question of the moon’s origin, heterogeneity, and circum-Mars material transport

Preliminary Results for the Multi-Robot, Multi-Partner, Multi-Mission, Planetary Exploration Analogue Campaign on Mount Etna

This paper was initially intended to report on the outcome of the twice postponed demonstration mission of the ARCHES project. Due to the global COVID pandemic, it has been postponed from 2020, then 2021, to 2022. Nevertheless, the development of our concepts and integration has progressed rapidly, and some of the preliminary results are worthwhile to share with the community to drive the dialog on robotics planetary exploration strategies. This paper includes an overview of the planned 4-week campaign, as well as the vision and relevance of the missiontowards the planned official space missions. Furthermore, the cooperative aspect of the robotic teams, the scientific motivation, the sub task achievements are summarised

Finally! Insights into the ARCHES Lunar Planetary Exploration Analogue Campaign on Etna in summer 2022

This paper summarises the first outcomes of the space demonstration mission of the ARCHES project which could have been performed this year from 13 june until 10 july on Italy’s Mt. Etna in Sicily. After the second postponement related to COVID from the initially for 2020 planed campaign, we are now very happy to report, that the whole campaign with more than 65 participants for four weeks has been successfully conduced. In this short overview paper, we will refer to all other publication here on IAC22. This paper includes an overview of the performed 4-week campaign and the achieved mission goals and first results but also share our findings on the organisational and planning aspects

Optische Sensoren für Fahrzeuge

Abbildenden optischen Sensoren wird in der Fahrzeugtechnik eine immer bedeutendere Rolle zukommen. Durch die Forderungen nach Erhöhung der Sicherheit im Auto- und Schienenverkehr, nach Verbesserung des Fahrkomforts und die Bestrebungen zur Erhöhung der Autonomie werden Sensorsysteme benötigt, die zuverlässig und in Echtzeit nutzerrelevante Informationen bereitstellen. Bei der Auslegung solcher Sensoren spielt der Systemgedanke eine entscheidende Rolle. Der Beitrag gibt einen Überblick über den aktuellen Entwicklungsstand auf dem Gebiet der momentan verfügbaren Sensortechnologien und über zukünftige Entwicklungen

Bias of ostracod stable isotope data caused by drying of sieve residues from water

Stable isotope analysis of ostracod shells is used routinely for palaeoenvironmental studies of ostracod-bearing records. Sample treatment usually involves the disaggregation of sediments and sieving; before the sieving residues were washed with water onto petri dishes and oven-dried. In our study, we compared δ18O and δ13C values of shells that were oven-dried from water and from ethanol alternatively. Large isotopic differences of up to 3‰ were determined for δ18O values, whereas differences in δ13C values were less pronounced with differences of up to 1.6‰. Stable isotope values of shells dried from water were lower for both oxygen and carbon as a result of calcite crystals precipitated on the shell surfaces during the drying process. Therefore, ostracod shells for stable isotope analysis should not be prepared by drying from water. Instead, shells should be dried from ethanol to obtain reliable stable isotope data; likewise freeze-drying is expected to provide trustworthy results