Occupancy mapping enables a mobile robot to make intelligent planning decisions to accomplish its tasks. Adaptive local maps is an algorithm which represents the occupancy information as a set of overlapping local maps anchored to poses in the robot's trajectory. At any time, a global occupancy map can be rendered from the local maps to be used for path planning. The advantage of this approach is that the occupancy information stays consistent despite the changes in the pose estimates resulting from loop closures and localization updates. The disadvantage, however, is that the number of local maps grows over time. For long robot runs, or for multiple runs in the same space, this growth will result in redundant occupancy information, which will in turn increase the time it takes to render the global map, as well as the memory footprint of the system. In this paper, we propose a novel approach for the maintenance of an adaptive local maps system, which intelligently prunes redundant local maps, ensuring the robustness and stability required for lifelong mapping.

The time complexity of making observations and loop closures in a graph-based visual SLAM system is a function of the number of views stored [1], [2]. Clever algorithms, such as approximate nearest neighbor search, can make this function sub-linear. Despite this, over time the number of views can still grow to a point at which the speed and/or accuracy of the system becomes unacceptable, especially in computation- and memory-constrained SLAM systems. However, not all views are created equal. Some views are rarely observed, because they have been created in an unusual lighting condition, or from low quality images, or in a location whose appearance has changed. These views can be removed to improve the overall performance of a SLAM system. In this paper, we propose a method for pruning views in a visual SLAM system to maintain its speed and accuracy for long term use.

We present a fast nonlinear approximation method for marginalizing out nodes on pose graphs for longterm simultaneous localization, mapping, and navigation. Our approximation preserves the pose graph structure to leverage the rich literature of pose graphs and optimization schemes. By re-parameterizing from absolute-to relative-pose spaces, our method does not suffer from the choice of linearization points as in previous works. We then join our approximation process with a scaled version of the recently-demoted pose-composition approach. Our approach eschews the expenses of many state-of-the-art convex optimization schemes through our efficient and simple $O(N^{2})$ implementation for a given known topology of the approximate subgraph. We demonstrate its speed and near optimality in practice by comparing against state-of-the-art techniques on popular datasets.

We introduce RoomsSeg, a novel method for segmenting occupancy grid maps into regions that represent rooms and corridors in the real world. The segmentation is utilized for systematic room-by-room cleaning on autonomous vacuum cleaners running in private homes. RoomsSeg is based on automated clutter removal and watershed segmentation on grid maps. Segmented regions are merged into rooms by semantic decision rules. Presented experimental results clearly indicate the efficiency and accuracy of the approach when compared with state of the art methods. When deployed on cleaning robots, a substantial decrease in mission time can be achieved.

The ASDEX Upgrade programme is directed towards physics input to critical elements of the ITER design and the preparation of ITER operation, as well as addressing physics issues for a future DEMO design. After the finalization of the tungsten coating of the plasma facing components, the re-availability of all flywheel-generators allowed high-power operation with up to 20 MW heating power at I p up to 1.2 MA. Implementation of alternative ECRH schemes (140 GHz O2- and X3-mode) facilitated central heating above n e = 1.2 × 1020 m−3 and low q 95 operation at B t = 1.8 T. Central O2-mode heating was successfully used in high P/R discharges with 20 MW total heating power and divertor load control with nitrogen seeding. Improved energy confinement is obtained with nitrogen seeding both for type-I and type-III ELMy conditions. The main contributor is increased plasma temperature, no significant changes in the density profile have been observed. This behaviour may be explained by higher pedestal temperatures caused by ion dilution in combination with a pressure limited pedestal and hollow nitrogen profiles. Core particle transport simulations with gyrokinetic calculations have been benchmarked by dedicated discharges using variations of the ECRH deposition location. The reaction of normalized electron density gradients to variations of temperature gradients and the T e/T i ratio could be well reproduced. Doppler reflectometry studies at the L–H transition allowed the disentanglement of the interplay between the oscillatory geodesic acoustic modes, turbulent fluctuations and the mean equilibrium E × B flow in the edge negative E r well region just inside the separatrix. Improved pedestal diagnostics revealed also a refined picture of the pedestal transport in the fully developed H-mode type-I ELM cycle. Impurity ion transport turned out to be neoclassical in between ELMs. Electron and energy transport remain anomalous, but exhibit different recovery time scales after an ELM. After recovery of the pre-ELM profiles, strong fluctuations develop in the gradients of n e and T e. The occurrence of the next ELM cannot be explained by the local current diffusion time scale, since this turns out to be too short. Fast ion losses induced by shear Alfvén eigenmodes have been investigated by time-resolved energy and pitch angle measurements. This allowed the separation of the convective and diffusive loss mechanisms.

Occupancy mapping is an important component of a robot navigation system since it enables intelligent planning of actions to accomplish a task. In some cases, occupancy information is mapped as part of a SLAM system. In other cases, such as visual SLAM, an occupancy map must be separately created, harnessing localization information. We present a novel occupancy mapping method that adaptively and efficiently takes advantage of improved trajectory estimates from the localization system, without requiring storage of all poses of the trajectory or the sensor information from all poses. Occupancy information is stored in local maps anchored to poses in the trajectory. Local maps are created adaptively depending on the uncertainty of localization. The local maps maintain consistency of occupancy information independently of changes in the localization map due to loop closures or convergence of the estimation algorithm. At any time, a global occupancy map (snapshot map) can be rendered from the local occupancy maps. We evaluate the performance of the system using real data and compare it to two baseline methods.

The upgraded electron cyclotron resonance heating (ECRH) system at ASDEX Upgrade (AUG) has been routinely used with eight gyrotrons during the last experimental campaign. A further upgrade will replace the existing system of four short-pulse (140 GHz, 2 s, 500 kW) gyrotrons. The final goal is to have around 6.5–7 MW at 140 GHz (or 5.5 MW at 105 GHz) from eight units available in the plasma during the whole AUG discharge (10 s). The system operates at 140 and 105 GHz with X2, O2 and X3 schemes. For B > 3 T also an ITER-like O1-scenario can be run using the 105 GHz option. Four of the eight launching antennas are capable of fast poloidal movements necessary for real-time control of the location of power deposition.

When the first edition of this book was published domestic robots were spoken of as a dream that was slowly becoming reality. At that time, in 2008, we looked back on more than twenty years of research and development in domestic robotics, especially in cleaning robotics. Although everybody expected cleaning to be the killer app for domestic robotics in the first half of these twenty years nothing big really happened. About ten years before the first edition of this book appeared, all of a sudden things started moving. Several small, but also some larger enterprises announced that they would soon launch domestic cleaning robots. The robotics community was anxiously awaiting these first cleaning robots and so were consumers. The big burst, however, was yet to come. The price tag of those cleaning robots was far beyond what people were willing to pay for a vacuum cleaner. It took another four years until, in 2002, a small and inexpensive device, which was not even called a cleaning robot, brought the first breakthrough: Roomba. Sales of the Roomba quickly passed the first million robots and increased rapidly. While for the first years after Roomba’s release, the big players remained on the sidelines, possibly to revise their own designs and, in particular their business models and price tags, some other small players followed quickly and came out with their own products. We reported about theses devices and their creators in the first edition. Since then the momentum in the field of domestics robotics has steadily increased. Nowadays most big appliance manufacturers have domestic cleaning robots in their portfolio. We are not only seeing more and more domestic cleaning robots and lawn mowers on the market, but we are also seeing new types of domestic robots, window cleaners, plant watering robots, tele-presence robots, domestic surveillance robots, and robotic sports devices. Some of these new types of domestic robots are still prototypes or concept studies. Others have already crossed the threshold to becoming commercial products. For the second edition of this chapter, we have decided to not only enumerate the devices that have emerged and survived in the past five years, but also to take a look back at how it all began, contrasting this retrospection with the burst of progress in the past five years in domestic cleaning robotics. We will not describe and discuss in detail every single cleaning robot that has seen the light of the day, but select those that are representative for the evolution of the technology as well as the market. We will also reserve some space for new types of mobile domestic robots, which will be the success stories or failures for the next edition of this chapter. Further we will look into nonmobile domestic robots, also called smart appliances, and examine their fate. Last but not least, we will look at the recent developments in the area of intelligent homes that surround and, at times, also control the mobile domestic robots and smart appliances described in the preceding sections.

Sensitive millimeter wave diagnostics need often to be protected against unwanted radiation like, for example, stray radiation from high power Electron Cyclotron Heating applied in nuclear fusion plasmas. A notch filter based on a waveguide Bragg reflector (photonic band-gap) may provide several stop bands of defined width within up to two standard waveguide frequency bands. A Bragg reflector that reflects an incident fundamental TE11 into a TM1n mode close to cutoff is combined with two waveguide tapers to fundamental waveguide diameter. Here the fundamental TE11 mode is the only propagating mode at both ends of the reflector. The incident TE11 mode couples through the taper and is converted to the high order TM1n mode by the Bragg structure at the specific Bragg resonances. The TM1n mode is trapped in the oversized waveguide section by the tapers. Once reflected at the input taper it will be converted back into the TE11 mode which then can pass through the taper. Therefore at higher order Bragg resonances, the filter acts as a reflector for the incoming TE11 mode. Outside of the Bragg resonances the TE11 mode can propagate through the oversized waveguide structure with only very small Ohmic attenuation compared to propagating in a fundamental waveguide. Coupling to other modes is negligible in the non-resonant case due to the small corrugation amplitude (typically 0.05.lambda(0), where lambda(0) is the free space wavelength). A Bragg reflector for 105 and 140 GHz was optimized by mode matching (scattering matrix) simulations and manufactured by SWISSto12 SA, where the required mechanical accuracy of +/- 5 mu m could be achieved by stacking stainless steel rings, manufactured by micro-machining, in a high precision guiding pipe. The two smooth-wall tapers were fabricated by electroforming. Several measurements were performed using vector network analyzers from Agilent (E8362B), ABmm (MVNA 8-350) and Rohde&Schwarz (ZVA24) together with frequency multipliers. The stop bands around 105 GHz (-55dB) and 140 GHz (-60dB) correspond to the TE11-TM12 and TE11-TM13 Bragg resonances. Experiments are in good agreement with theory.

Vector field SLAM is a framework for localizing a mobile robot in an unknown environment by learning the spatial distribution of continuous signals such as those emitted by WiFi or active beacons. In our previous work we showed that this approach is capable of keeping a robot localized in small to medium sized areas, e.g. in a living room, where four continuous signals of an active beacon are measured (Gutmann et al., 2012). In this article we extend the method to larger environments up to the size of a complete home by deploying more signal sources for covering the expanded area. We first analyze the complexity of vector field SLAM with respect to area size and number of signals and then describe an approximation that divides the localization map into decoupled sub-maps to keep memory and run-time requirements low. We also describe a method for re-localizing the robot in a vector field previously mapped. This enables a robot to resume its navigation after it has been kidnapped or paused and resumed. The re-localization method is evaluated in a standard test environment and shows an average position accuracy of 10 to 35 cm with a localization success rate of 96 to 99%. Additional experimental results from running the system in houses of up to 125 m^2 demonstrate the performance of our approach. The presented methods are suitable for commercial low-cost products including robots for autonomous and systematic floor cleaning.

Consumer robotic devices provide a platform for embedded computer vision algorithms in applications for everyday life. The consumer market is very price-sensitive, so robots must be developed with a single task in mind, aiming to provide the best performance at the lowest cost. Computational resources in consumer robotics are scarce given cost constraints, forcing the design of novel algorithms that elegantly incorporate such constraints. We present a graph-based SLAM approach designed to operate on computationally constrained platforms using monocular vision and odometry. When computation and memory are limited, visual tracking becomes difficult or impossible, and costs for map representation and updating must remain low. Our system constructs a map of structured views using only weak temporal assumptions and performs recognition and relative pose estimation over the set of views. We fuse visual observations and differential measurements in an incrementally optimized graph representation. Using variable elimination and constraint pruning, graph complexity and storage is kept linear in explored space rather than growing over time. We evaluate performance on sequences with ground truth and also compare to a standard graph-SLAM approach.