Assessing temporal behavior in lidar point clouds of urban environments

Self-driving cars and robots that run autonomously over long periods of time need high-precision and up-to-date models of the changing environment. The main challenge for creating long term maps of dynamic environments is to identify changes and adapt the map continuously. Changes can occur abruptly, gradually, or even periodically. In this work, we investigate how dense mapping data of several epochs can be used to identify the temporal behavior of the environment. This approach anticipates possible future scenarios where a large fleet of vehicles is equipped with sensors which continuously capture the environment. This data is then being sent to a cloud based infrastructure, which aligns all datasets geometrically and subsequently runs scene analysis on it, among these being the analysis for temporal changes of the environment. Our experiments are based on a LiDAR mobile mapping dataset which consists of 150 scan strips (a total of about 1 billion points), which were obtained in multiple epochs. Parts of the scene are covered by up to 28 scan strips. The time difference between the first and last epoch is about one year. In order to process the data, the scan strips are aligned using an overall bundle adjustment, which estimates the surface (about one billion surface element unknowns) as well as 270,000 unknowns for the adjustment of the exterior orientation parameters. After this, the surface misalignment is usually below one centimeter. In the next step, we perform a segmentation of the point clouds using a region growing algorithm. The segmented objects and the aligned data are then used to compute an occupancy grid which is filled by tracing each individual LiDAR ray from the scan head to every point of a segment. As a result, we can assess the behavior of each segment in the scene and remove voxels from temporal objects from the global occupancy grid.DFG/GRK/215

Binary planetesimals and their role in planet formation

One of the main evolutionary stages of planet formation is the dynamical evolution of planetesimal disks. These disks are thought to evolve through gravitational encounters and physical collisions between single planetesimals. In recent years, many binary planetesimals have been observed in the Solar system, indicating that the binarity of planetesimals is high. However, current studies of planetesimal disks formation and evolution do not account for the role of binaries. Here we point out that gravitational encounters of binary planetesimals can have an important role in the evolution of planetesimal disks. Binary planetesimals catalyze close encounters between planetesimals, and can strongly enhance their collision rate. Binaries may also serve as additional heating source of the planetesimal disk, through the exchange of the binaries gravitational potential energy into the kinetic energy of planetesimals in the disk.Comment: Accepted to ApJ

Prograde rotation of protoplanets by accretion of pebbles in a gaseous environment

We perform hydrodynamical simulations of the accretion of pebbles and rocks onto protoplanets of a few hundred kilometres in radius, including two-way drag force coupling between particles and the protoplanetary disc gas. Particle streams interacting with the gas far out within the Hill sphere of the protoplanet spiral into a prograde circumplanetary disc. Material is accreted onto the protoplanet due to stirring by the turbulent surroundings. We speculate that the trend for prograde rotation among the largest asteroids is primordial and that protoplanets accreted 10%-50% of their mass from pebbles and rocks during the gaseous solar nebula phase. Our model also offers a possible explanation for the narrow range of spin periods observed among the largest bodies in the asteroid and trans-Neptunian belts, and predicts that 1000 km-scale Kuiper belt objects that have not experienced giant impacts should preferentially spin in the prograde direction.Comment: Accepted for publication in MNRAS. Minor changes in response to referee report, general language changes throughout the pape

Vehicle localization by lidar point correlation improved by change detection

LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like cars, pedestrians or even construction sites could lead to wrong localization results, we use a change detection algorithm to detect these objects in the reference data. If an object occurs in a certain number of measurements at the same position, we mark it and every containing point as static. In the next step, we merge the data of the single measurement epochs to one reference dataset, whereby we only use static points. Further, we also use a classification algorithm to detect trees. For the online localization of the vehicle, we use simulated data of a vertical aligned automotive LiDAR sensor. As we only want to use static objects in this case as well, we use a random forest classifier to detect dynamic scan points online. Since the automotive data is derived from the LiDAR Mobile Mapping System, we are able to use the labelled objects from the reference data generation step to create the training data and further to detect dynamic objects online. The localization then can be done by a point to image correlation method using only static objects. We achieved a localization standard deviation of about 5 cm (position) and 0.06° (heading), and were able to successfully localize the vehicle in about 93 % of the cases along a trajectory of 13 km in Hannover, Germany

Bridging the gap between omics and earth system science to better understand how environmental change impacts marine microbes

The advent of genomic-, transcriptomic- and proteomic-based approaches has revolutionized our ability to describe marine microbial communities, including biogeography, metabolic potential and diversity, mechanisms of adaptation, and phylogeny and evolutionary history. New interdisciplinary approaches are needed to move from this descriptive level to improved quantitative, process-level understanding of the roles of marine microbes in biogeochemical cycles and of the impact of environmental change on the marine microbial ecosystem. Linking studies at levels from the genome to the organism, to ecological strategies and organism and ecosystem response, requires new modelling approaches. Key to this will be a fundamental shift in modelling scale that represents micro-organisms from the level of their macromolecular components. This will enable contact with omics data sets and allow acclimation and adaptive response at the phenotype level (i.e. traits) to be simulated as a combination of fitness maximization and evolutionary constraints. This way forward will build on ecological approaches that identify key organism traits and systems biology approaches that integrate traditional physiological measurements with new insights from omics. It will rely on developing an improved understanding of ecophysiology to understand quantitatively environmental controls on microbial growth strategies. It will also incorporate results from experimental evolution studies in the representation of adaptation. The resulting ecosystem-level models can then evaluate our level of understanding of controls on ecosystem structure and function, highlight major gaps in understanding and help prioritize areas for future research programs. Ultimately, this grand synthesis should improve predictive capability of the ecosystem response to multiple environmental drivers

Element redistribution along hydraulic and redox gradients of low-centered polygons, Lena Delta, northern Siberia

Wetland soils affected by permafrost are extensive in subarctic and arctic tundra. However, this fact does not imply these soils have been sufficiently investigated. In particular, studies of element translocation processes are scarce. This study was conducted (i) to determine the relationship between water and redox regimes in wetland soils in the Siberian tundra, and (ii) to investigate their influence on the distribution of redox sensitive and associate elements (Mn, Fe, P). Major geomorphic units were chosen (microhigh, polygon rim and slope; microlow, polygon center) from two low-centered polygons in the Lena Delta. Within polygons, redox potential, permafrost, and water level were measured during summer in 1999 and 2000 and (related) compared with element distribution. Manganese, Fe, and P accumulations were preferentially observed in aerobic microhighs. Anaerobic conditions in the microlows lead to a mobilization of Mn, Fe, and P. The elements migrate via water and are immobilized at the microhigh, which acts as an oxidative barrier. The element pattern, indicating an upward flux via water along redox gradients, is explained by higher evapotranspiration from soils and vegetation of the microhighs (Typic Aquiturbel) compared with soils and vegetation of the microlows (Typic Historthel). However, in further research this upward transport should be validated using labeled elements

Terahertz Active Photonic Crystals for Condensed Gas Sensing

The terahertz (THz) spectral region, covering frequencies from 1 to 10 THz, is highly interesting for chemical sensing. The energy of rotational and vibrational transitions of molecules lies within this frequency range. Therefore, chemical fingerprints can be derived, allowing for a simple detection scheme. Here, we present an optical sensor based on active photonic crystals (PhCs), i.e., the pillars are fabricated directly from an active THz quantum-cascade laser medium. The individual pillars are pumped electrically leading to laser emission at cryogenic temperatures. There is no need to couple light into the resonant structure because the PhC itself is used as the light source. An injected gas changes the resonance condition of the PhC and thereby the laser emission frequency. We achieve an experimental frequency shift of 10−3 times the center lasing frequency. The minimum detectable refractive index change is 1.6 × 10−5 RIU

IL4I1 Is a Metabolic Immune Checkpoint that Activates the AHR and Promotes Tumor Progression

Immune checkpoint blockade therapy induces the AHR-activating enzyme IL4I1, which promotes tumor progression, through its effects on tumor cell motility and adaptive immunity

A Coin Vibrational Motor Swimming at Low Reynolds Number

Low-cost coin vibrational motors, used in haptic feedback, exhibit rotational internal motion inside a rigid case. Because the motor case motion exhibits rotational symmetry, when placed into a fluid such as glycerin, the motor does not swim even though its oscillatory motions induce steady streaming in the fluid. However, a piece of rubber foam stuck to the curved case and giving the motor neutral buoyancy also breaks the rotational symmetry allowing it to swim. We measured a 1 cm diameter coin vibrational motor swimming in glycerin at a speed of a body length in 3 seconds or at 3 mm/s. The swim speed puts the vibrational motor in a low Reynolds number regime similar to bacterial motility, but because of the oscillations of the motor it is not analogous to biological organisms. Rather the swimming vibrational motor may inspire small inexpensive robotic swimmers that are robust as they contain no external moving parts. A time dependent Stokes equation planar sheet model suggests that the swim speed depends on a steady streaming velocity V stream ~ Re 1/2s U 0 where U 0 is the velocity of surface oscillations, and streaming Reynolds number Re s = U 20/(ων) for motor angular frequency ω and fluid kinematic viscosity ν