Visual SLAM for flying vehicles

The ability to learn a map of the environment is important for numerous types of robotic vehicles. In this paper, we address the problem of learning a visual map of the ground using flying vehicles. We assume that the vehicles are equipped with one or two low-cost downlooking cameras in combination with an attitude sensor. Our approach is able to construct a visual map that can later on be used for navigation. Key advantages of our approach are that it is comparably easy to implement, can robustly deal with noisy camera images, and can operate either with a monocular camera or a stereo camera system. Our technique uses visual features and estimates the correspondences between features using a variant of the progressive sample consensus (PROSAC) algorithm. This allows our approach to extract spatial constraints between camera poses that can then be used to address the simultaneous localization and mapping (SLAM) problem by applying graph methods. Furthermore, we address the problem of efficiently identifying loop closures. We performed several experiments with flying vehicles that demonstrate that our method is able to construct maps of large outdoor and indoor environments. © 2008 IEEE

Efficient exploration of unknown indoor environments using a team of mobile robots

Whenever multiple robots have to solve a common task, they need to coordinate their actions to carry out the task efficiently and to avoid interferences between individual robots. This is especially the case when considering the problem of exploring an unknown environment with a team of mobile robots. To achieve efficient terrain coverage with the sensors of the robots, one first needs to identify unknown areas in the environment. Second, one has to assign target locations to the individual robots so that they gather new and relevant information about the environment with their sensors. This assignment should lead to a distribution of the robots over the environment in a way that they avoid redundant work and do not interfere with each other by, for example, blocking their paths. In this paper, we address the problem of efficiently coordinating a large team of mobile robots. To better distribute the robots over the environment and to avoid redundant work, we take into account the type of place a potential target is located in (e.g., a corridor or a room). This knowledge allows us to improve the distribution of robots over the environment compared to approaches lacking this capability. To autonomously determine the type of a place, we apply a classifier learned using the AdaBoost algorithm. The resulting classifier takes laser range data as input and is able to classify the current location with high accuracy. We additionally use a hidden Markov model to consider the spatial dependencies between nearby locations. Our approach to incorporate the information about the type of places in the assignment process has been implemented and tested in different environments. The experiments illustrate that our system effectively distributes the robots over the environment and allows them to accomplish their mission faster compared to approaches that ignore the place labels

Conceptual spatial representations for indoor mobile robots

We present an approach for creating conceptual representations of human-made indoor environments using mobile robots. The concepts refer to spatial and functional properties of typical indoor environments. Following findings in cognitive psychology, our model is composed of layers representing maps at different levels of abstraction. The complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition. The system also incorporates a linguistic framework that actively supports the map acquisition process, and which is used for situated dialogue. Finally, we discuss the capabilities of the integrated system

An Autonomous Robotic System for Mapping Abandoned Mines

We present the software architecture of a robotic system for mapping abandoned mines. The software is capable of acquiring consistent 2D maps of large mines with many cycles, represented as Markov random fields. 3D C-space maps are acquired from local 3D range scans, which are used to identify navigable paths using A* search. Our system has been deployed in three abandoned mines, two of which inaccessible to people, where it has acquired maps of unprecedented detail and accuracy

Autonomous Exploration for 3D Map Learning

Abstract. Autonomous exploration is a frequently addressed problem in the ro-botics community. This paper presents an approach to mobile robot exploration that takes into account that the robot acts in the three-dimensional space. Our approach can build compact three-dimensional models autonomously and is able to deal with negative obstacles such as abysms. It applies a decision-theoretic framework which considers the uncertainty in the map to evaluate potential ac-tions. Thereby, it trades off the cost of executing an action with the expected information gain taking into account possible sensor measurements. We present experimental results obtained with a real robot and in simulation.

Interplay of gravitation and linear superposition of different mass eigenstates

The interplay of gravitation and the quantum-mechanical principle of linear superposition induces a new set of neutrino oscillation phases. These ensure that the flavor-oscillation clocks, inherent in the phenomenon of neutrino oscillations, redshift precisely as required by Einstein's theory of gravitation. The physical observability of these phases in the context of the solar neutrino anomaly, type-II supernovae, and certain atomic systems is briefly discussed

MVCSLAM: Mono-Vision Corner SLAM for Autonomous Micro-Helicopters in GPS Denied Environments

We present a real-time vision navigation and ranging method (VINAR) for the purpose of Simultaneous Localization and Mapping (SLAM) using monocular vision. Our navigation strategy assumes a GPS denied unknown environment, whose indoor architecture is represented via corner based feature points obtained through a monocular camera. We experiment on a case study mission of vision based SLAM through a conventional maze of corridors in a large building with an autonomous Micro Aerial Vehicle (MAV). We propose a method for gathering useful landmarks from a monocular camera for SLAM use. We make use of the corners by exploiting the architectural features of the manmade indoors

[89Zr]Zr-PSMA-617 PET/CT in biochemical recurrence of prostate cancer : first clinical experience from a pilot study including biodistribution and dose estimates

Purpose Prostate-specific membrane antigen (PSMA)-targeted PET/CT has become increasingly important in the management of prostate cancer, especially in localization of biochemical recurrence (BCR). PSMA-targeted PET/CT imaging with long-lived radionuclides as 89Zr (T1/2=78.4 h) may improve diagnostics by allowing data acquisition on later time points. In this study, we present our frst clinical experience including preliminary biodistribution and dosimetry data of [ 89Zr]Zr-PSMA-617 PET/CT in patients with BCR of prostate cancer. Methods Seven patients with BCR of prostate cancer who revealed no (n =4) or undetermined (n =3) findings on [ 68Ga]Ga-PSMA-11 PET/CT imaging were referred to [ 89Zr]Zr-PSMA-617 PET/CT. PET/CT imaging was performed 1 h, 24 h, 48 h, and 72 h post injection (p.i.) of 111±11 MBq [ 89Zr]Zr-PSMA-617 (mean±standard deviation). Normal organ distribution and dosimetry were determined. Lesions visually considered as suggestive of prostate cancer were quantitatively analyzed. Results Intense physiological uptake was observed in the salivary and lacrimal glands, liver, spleen, kidneys, intestine and urinary tract. The parotid gland received the highest absorbed dose (0.601±0.185 mGy/MBq), followed by the kidneys (0.517±0.125 mGy/MBq). The estimated overall efective dose for the administration of 111 MBq was 10.1 mSv (0.0913±0.0118 mSv/MBq). In 6 patients, and in particular in 3 of 4 patients with negative [ 68Ga]Ga-PSMA-11 PET/CT, at least one prostate cancer lesion was detected in [ 89Zr]Zr-PSMA-617 PET/CT imaging at later time points. The majority of tumor lesions were frst visible at 24 h p.i. with continuously increasing tumor-to-background ratio over time. All tumor lesions were detectable at 48 h and 72 h p.i. Conclusion [ 89Zr]Zr-PSMA-617 PET/CT imaging is a promising new diagnostic tool with acceptable radiation exposure for patients with prostate cancer especially when [ 68Ga]Ga-PSMA-11 PET/CT imaging fails detecting recurrent disease. The long half-life of 89Zr enables late time point imaging (up to 72 h in our study) with increased tracer uptake in tumor lesions and higher tumor-to-background ratios allowing identifcation of lesions non-visible on [ 68Ga]Ga-PSMA-11 PET/CT imaging