Indoor Localization Using Radio, Vision and Audio Sensors: Real-Life Data Validation and Discussion

This paper investigates indoor localization methods using radio, vision, and audio sensors, respectively, in the same environment. The evaluation is based on state-of-the-art algorithms and uses a real-life dataset. More specifically, we evaluate a machine learning algorithm for radio-based localization with massive MIMO technology, an ORB-SLAM3 algorithm for vision-based localization with an RGB-D camera, and an SFS2 algorithm for audio-based localization with microphone arrays. Aspects including localization accuracy, reliability, calibration requirements, and potential system complexity are discussed to analyze the advantages and limitations of using different sensors for indoor localization tasks. The results can serve as a guideline and basis for further development of robust and high-precision multi-sensory localization systems, e.g., through sensor fusion and context and environment-aware adaptation.Comment: 6 pages, 6 figure

Trilateration Using Motion Models

In this paper, we present a framework for doing localization from distance measurements, given an estimate of the local motion. We show how we can register the local motion of a receiver, to a global coordinate system, using trilateration of given distance measurements from the receivers to senders in known positions. We describe how many different motion models can be formulated within the same type of registration framework, by only changing the transformation group. The registration is based on a test and hypothesis framework, such as RANSAC, and we present novel and fast minimal solvers that can be used to bootstrap such methods. The system is tested on both synthetic and real data with promising results

Sensor node calibration in presence of a dominant reflective plane

Recent advances in simultaneous estimation of both receiver and sender positions in ad-hoc sensor networks have made it possible to automatically calibrate node positions - a prerequisite for many applications. In man-made environments there are often large planar reflective surfaces that give significant reverberations. In this paper, we study geometric problems of receiver-sender node calibration in the presence of such reflective planes. We establish a rank-1 factorization problem that can be used to simplify the estimation. We also show how to estimate offsets, in the Time difference of arrival case, using only the rank constraint. Finally, we present a new solver for the minimal cases of sender-receiver position estimation. These contributions result in a powerful stratified approach for the node calibration problem, given a reflective plane. The methods are verified with both synthetic and real data

Minimal Solvers for Point Cloud Matching with Statistical Deformations

An important issue in simultaneous localisation and mapping is how to match and merge individual local maps into one global map. This is addressed within the field of robotics and is crucial for multi-robot SLAM. There are a number of different ways to solve this task depending on the representation of the map. To take advantage of matching and merging methods that allow for deformations of the local maps it is important to find feature matches that capture such deformations. In this paper we present minimal solvers for point cloud matching using statistical deformations. The solvers use either three or four point matches. These solve for either rigid or similarity transformation as well as shape deformation in the direction of the most important modes of variation. Given an initial set of tentative matches based on, for example, feature descriptors or machine learning we use these solvers in a RANSAC loop to remove outliers among the tentative matches. We evaluate the methods on both synthetic and real data and compare them to RANSAC methods based on Procrustes and demonstrate that the proposed methods improve on the current state-of-the-art

Management and Long Term Archiving of Remote Sensing and In-situ Data at DFD - Status and Trends

Within the last few years the situation of satellite and airborne supported remote sensing has changed fundamentally. The reasons can be explained among others by the following aspects: • technical and technological developments and factors, such as the increase of the number of earth observation missions (e.g. multiple satellite missions on different or on one orbit), new advanced sensor technologies (e.g. SAR- and hyperspectral sensors of high spatial resolution), new storage media and storage technologies, and new improved data processing (e.g. automated interpretation algorithms and processing chains), • market-oriented aspects (e.g. private remote sensing missions, such as QuickBird or RapidEye). In addition to this the development was politically forced by European Union (EU) and European Space Agency (ESA) having established the program "Global monitoring for environment and Security" (COPERNICUS; erstwhile called GMES), which serves the development of an operative European Earth observation satellite fleet to combine earth observation monitoring with airborne remote sensing supported by terrestrial, maritime in-situ-measuring networks and additional data sources in operative process chains as well as services. Besides the continuous provisioning of user friendly and reliable earth observation services e.g. to climate protection, for ecological survey, humanitarian assistance or for the reply to security-relevant questions the COPERNICUS initiative is aimed at the establishment of a European market for innovative remote sensing based services. TERrestrial ENvironmental Observatories (TERENO) - Initiative of the Helmholtz community, operates on a temporal and spatial scale that make i.) integration of remote sensing technologies and ii.) the combination of these technologies with in-situ-measurement technologies urgently needed. Both aspects require an advanced management and archiving design including appropriate technology and technological infrastructure to ensure a reliable long-term archiving. In the lecture, the DFD will present its solution the Data and Information Management System (DIMS) including its experience in archiving large data-massive. For a data provision to solute scientific questions in distant future additional aspects will be considered like data curation and interoperability in a service oriented environment

Data Management and Long Term Archiving of remote Sensing and In-situ Data at DFD - Status and Trends

Aktueller Stand und Ausblick auf die Langzeitarchivierung von Satellitendaten zur Erdbeobachtung und In-situ Daten im NRSDA (National Remote Sensing Data Archiv

The Multi-view Geometry of Parallel Cylinders

In this paper we study structure from motion problems for parallel cylinders. Using sparse keypoint correspondences is an efficient (and standard) way to solve the structure from motion problem. However, point features are sometimes unavailable and they can be unstable over time and viewing conditions. Instead, we propose a framework based on silhouettes of quadric surfaces, with special emphasis on parallel cylinders. Such structures are quite common, e.g. trees, lampposts, pillars, and furniture legs. Traditionally, the projection of the center lines of such cylinders have been considered and used in computer vision. Here, we demonstrate that the apparent width of the cylinders also contains useful information for structure and motion estimation. We provide mathematical analysis of relative structure and relative motion tensors, which is used to develop a number of minimal solvers for simultaneously estimating camera pose and scene structure from silhouette lines of cylinders. These solvers can be used efficiently in robust estimation schemes, such as RANSAC. We use Sampson-approximation methods for efficient estimation using over-determined data and develop averaging techniques. We also perform synthetic accuracy and robustness tests and evaluate our methods on a number of real-world scenarios