Optimization Based Self-localization for IoT Wireless Sensor Networks

In this paper we propose an embedded optimization framework for the simultaneous self-localization of all sensors in wireless sensor networks making use of range measurements from ultra-wideband (UWB) signals. Low-power UWB radios, which provide time-of-arrival measurements with decimeter accuracy over large distances, have been increasingly envisioned for realtime localization of IoT devices in GPS-denied environments and large sensor networks. In this work, we therefore explore different non-linear least-squares optimization problems to formulate the localization task based on UWB range measurements. We solve the resulting optimization problems directly using non-linear-programming algorithms that guarantee convergence to locally optimal solutions. This optimization framework allows the consistent comparison of different optimization methods for sensor localization. We propose and demonstrate the best optimization approach for the self-localization of sensors equipped with off-the-shelf microcontrollers using state-of-the-art code generation techniques for the plug-and-play deployment of the optimal localization algorithm. Numerical results indicate that the proposed approach improves localization accuracy and decreases computation times relative to existing iterative methods

A tool for crowdsourced building information modeling through low-cost range camera: preliminary demonstration and potential

Within the construction sector, Building Information Models (BIMs) are more and more used thanks to the several benefits that they offer in the design of new buildings and the management of the existing ones. Frequently, however, BIMs are not available for already built constructions, but, at the same time, the range camera technology provides nowadays a cheap, intuitive and effective tool for automatically collecting the 3D geometry of indoor environments. It is thus essential to find new strategies, able to perform the first step of the scan to BIM process, by extracting the geometrical information contained in the 3D models that are so easily collected through the range cameras. In this work, a new algorithm to extract planimetries from the 3D models of rooms acquired by means of a range camera is therefore presented. The algorithm was tested on two rooms, characterized by different shapes and dimensions, whose 3D models were captured with the Occipital Structure SensorTM. The preliminary results are promising: the developed algorithm is able to model effectively the 2D shape of the investigated rooms, with an accuracy level comprised in the range of 5 - 10 cm. It can be potentially used by non-expert users in the first step of the BIM generation, when the building geometry is reconstructed, for collecting crowdsourced indoor information in the frame of BIMs Volunteered Geographic Information (VGI) generation

Attention and Anticipation in Fast Visual-Inertial Navigation

We study a Visual-Inertial Navigation (VIN) problem in which a robot needs to estimate its state using an on-board camera and an inertial sensor, without any prior knowledge of the external environment. We consider the case in which the robot can allocate limited resources to VIN, due to tight computational constraints. Therefore, we answer the following question: under limited resources, what are the most relevant visual cues to maximize the performance of visual-inertial navigation? Our approach has four key ingredients. First, it is task-driven, in that the selection of the visual cues is guided by a metric quantifying the VIN performance. Second, it exploits the notion of anticipation, since it uses a simplified model for forward-simulation of robot dynamics, predicting the utility of a set of visual cues over a future time horizon. Third, it is efficient and easy to implement, since it leads to a greedy algorithm for the selection of the most relevant visual cues. Fourth, it provides formal performance guarantees: we leverage submodularity to prove that the greedy selection cannot be far from the optimal (combinatorial) selection. Simulations and real experiments on agile drones show that our approach ensures state-of-the-art VIN performance while maintaining a lean processing time. In the easy scenarios, our approach outperforms appearance-based feature selection in terms of localization errors. In the most challenging scenarios, it enables accurate visual-inertial navigation while appearance-based feature selection fails to track robot's motion during aggressive maneuvers.Comment: 20 pages, 7 figures, 2 table