Global Localization in Unstructured Environments using Semantic Object Maps Built from Various Viewpoints

We present a novel framework for global localization and guided relocalization of a vehicle in an unstructured environment. Compared to existing methods, our pipeline does not rely on cues from urban fixtures (e.g., lane markings, buildings), nor does it make assumptions that require the vehicle to be navigating on a road network. Instead, we achieve localization in both urban and non-urban environments by robustly associating and registering the vehicle's local semantic object map with a compact semantic reference map, potentially built from other viewpoints, time periods, and/or modalities. Robustness to noise, outliers, and missing objects is achieved through our graph-based data association algorithm. Further, the guided relocalization capability of our pipeline mitigates drift inherent in odometry-based localization after the initial global localization. We evaluate our pipeline on two publicly-available, real-world datasets to demonstrate its effectiveness at global localization in both non-urban and urban environments. The Katwijk Beach Planetary Rover dataset is used to show our pipeline's ability to perform accurate global localization in unstructured environments. Demonstrations on the KITTI dataset achieve an average pose error of 3.8m across all 35 localization events on Sequence 00 when localizing in a reference map created from aerial images. Compared to existing works, our pipeline is more general because it can perform global localization in unstructured environments using maps built from different viewpoints.Comment: 8 pages, 6 figures, presented at IROS 202

Find your Way by Observing the Sun and Other Semantic Cues

In this paper we present a robust, efficient and affordable approach to self-localization which does not require neither GPS nor knowledge about the appearance of the world. Towards this goal, we utilize freely available cartographic maps and derive a probabilistic model that exploits semantic cues in the form of sun direction, presence of an intersection, road type, speed limit as well as the ego-car trajectory in order to produce very reliable localization results. Our experimental evaluation shows that our approach can localize much faster (in terms of driving time) with less computation and more robustly than competing approaches, which ignore semantic information

Integration of GIS and DSS: a methodology to evaluate low carbon strategies in a smart urban metabolism context

An Urban Metabolism system can be examined by evaluating the incoming and outgoing energy flows of a city. Academics and researchers have utilized Urban Metabolism framework to analyze different urban areas and have begun to extend the framework beyond the city-region unit of analysis to inform related aspects of the Urban Metabolism: in this context UM framework is a tool that can be useful in the decision making process. This study aims to be an opportunity and an example of environmental analysis of UM, from the point of view of CO2eq emissions and absorptions. A multi-objective Decision Support System is developed with the aim of minimizing the environmental, social and economic impacts of the CO2eq emissions at the municipal level. The Decision Support System has been implemented and a few scenario analyses were developed: enhancement of energy efficiency of residential and industrial buildings, increase of green areas, production of electricity by means of photovoltaic installation on site, efficiency of the vehicle fleet and finally, proper recycling of waste. The municipality of Tavagnacco recognizes this approach as a new perspective of analysis for a future comparison project with other municipalities. From this comparison it is expected to get results that can accredit the most convenient method from the environmental, social and economic point of view, and can offer the basis for the improvement of energy efficiency. Results of this work can provide evidence in support of an increased awareness in issues related to the CO2eq reduction.Il metabolismo di un sistema urbano pu`o essere esaminato cercando di sviluppare e comprendere i flussi energetici in ingresso e in uscita dalla citt`a. Accademici e ricercatori hanno utilizzato questo approccio al fine di valutare diverse aree urbane e hanno recentemente esteso il quadro di indagine al di l`a dell\u2019unit`a di citt`a-regione al fine di utilizzare questo strumento nell\u2019ambito del processo decisionale di pianificazione del territorio. Questo percorso vuole definire una possibile metodologia e un esempio di approccio spaziale ad un\u2019analisi di bilancio comunale di CO2eq. E\u2019 stato sviluppato un Sistema di Supporto alle Decisioni multiobiettivo, con il fine di minimizzare l\u2019impatto ambientale oltre a quello sociale e quello economico delle emissioni di CO2eq su scala comunale. Il Sistema di Supporto alle Decisioni ha previsto l\u2019implementazione di alcuni scenari di analisi quali l\u2019incentivazione dell\u2019efficientamento energetico degli edi- fici residenziali ma anche industriali, l\u2019aumento delle aree a verde, la produzione di energia elettrica in loco mediante impianto fotovoltaico, l\u2019efficientamento del parco veicolare e infine una valida raccolta differenziata. Il comune di Tavagnacco conosce le sfide future in merito ai problemi ambientali e si impegna in un progetto pilota di valutazione delle emissioni di CO2eq. In un prossimo futuro si delinea un lavoro di confronto tra comuni che utilizzano metodi di abbattimento delle emissioni. Da questo confronto ci si aspetta di ottenere risultati che possano accreditare il metodo pi`u conveniente dal punto di vista ambientale, economico e sociale, e quindi offrire delle basi per una valutazione sull\u2019opportunit`a di miglioramento ed efficientamento energetico a livello comunale e sovracomunale. Si auspica che i risultati di questo lavoro possano offrire elementi convincenti a supporto di un atteggiamento sempre pi`u attento alle problematiche legate alla riduzione delle emissioni di CO2eq

Software Porting of a 3D Reconstruction Algorithm to Razorcam Embedded System on Chip

A method is presented to calculate depth information for a UAV navigation system from Keypoints in two consecutive image frames using a monocular camera sensor as input and the OpenCV library. This method was first implemented in software and run on a general-purpose Intel CPU, then ported to the RazorCam Embedded Smart-Camera System and run on an ARM CPU onboard the Xilinx Zynq-7000. The results of performance and accuracy testing of the software implementation are then shown and analyzed, demonstrating a successful port of the software to the RazorCam embedded system on chip that could potentially be used onboard a UAV with tight constraints of size, weight, and power. The potential impacts will be seen through the continuation of this research in the Smart ES lab at University of Arkansas

A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles

Deep learning is recently showing outstanding results for solving a wide variety of robotic tasks in the areas of perception, planning, localization, and control. Its excellent capabilities for learning representations from the complex data acquired in real environments make it extremely suitable for many kinds of autonomous robotic applications. In parallel, Unmanned Aerial Vehicles (UAVs) are currently being extensively applied for several types of civilian tasks in applications going from security, surveillance, and disaster rescue to parcel delivery or warehouse management. In this paper, a thorough review has been performed on recent reported uses and applications of deep learning for UAVs, including the most relevant developments as well as their performances and limitations. In addition, a detailed explanation of the main deep learning techniques is provided. We conclude with a description of the main challenges for the application of deep learning for UAV-based solutions