다중 로봇 SLAM을 위한 상관관계 기반 지도병합 기술

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2013. 8. 이범희.Multi-robot simultaneous localization and mapping (SLAM) is an advanced technique used by multiple robots and autonomous vehicles to build up a collective map within an unknown environment, or to update a collective map within a known environment, while at the same time keeping track of their current location. The collective map is obtained by merging individual maps built by different multiple robots exploring the environment. When robots do not know their initial poses one another, the problem of map merging becomes challenging because the robots have different coordinate systems. If robot-to-robot measurements are not available, the problem of map merging becomes more challenging because the map transformation matrix (MTM) among robots cannot be computed directly. This dissertation presents novel map merging techniques based on the analysis of the correlation among the individual maps, which do not need the knowledge of the relative initial poses of robots and the robot-to-robot measurements. After the cross-correlation function among the spectrometric or tomographic information extracted from the individual maps is generated, the MTM is computed by taking the rotation angle and the translation amounts corresponding to the maximum cross-correlation values. The correlation-based map merging techniques with spectral information presented in this dissertation are the extensions of a conventional map merging technique. One extension is spectrum-based feature map merging (SFMM), which extracts the spectral information of feature maps from virtual supporting lines and computes the MTM by matching the extracted spectral information. The other extension is enhanced-spectrum-based map merging (ESMM), which enhances grid maps using the locations of visual objects and computes the MTM by matching the spectral information extracted from the enhanced grid maps. The two extensions overcome successfully the limitation of the conventional map merging technique. The correlation-based map merging technique with tomographic information is a new map merging technique, which is named tomographic map merging (TMM). Since tomographic analysis can provide more detailed information on grid maps according to rotation and translation than spectral analysis, the more accurate MTM can be computed by matching the tomographic information. The TMM was tested on various pairs of partial maps from real experiments in indoor and outdoor environments. The improved accuracy was verified by showing smaller map merging errors than the conventional map merging technique and several existing map merging techniques.Chapter 1 Introduction 1.1 Background and motivation 1.2 Related works 1.3 Contributions 1.4 Organization Chapter 2 Multi-Robot SLAM and Map Merging 2.1 SLAM using Particle Filters 2.2 Multi-Robot SLAM (MR-SLAM) 2.2.1 MR-SLAM with Known Initial Correspondences 2.2.2 MR-SLAM with Unknown Initial Correspondences 2.3 Map Merging Chapter 3 Map Merging based on Spectral Correlation 3.1 Spectrum-based Map Merging (SMM) 3.2 Spectrum-based Feature Map Merging (SFMM) 3.2.1 Overview of the SFMM 3.2.2 Problem Formulation for the SFMM 3.2.3 Virtual Supporting Lines (VSLs) 3.2.4 Estimation of Map Rotation with Hough Spectra 3.2.5 Rasterization of Updated Feature Maps with VSLs 3.2.6 Estimation of Map Displacements 3.3 Enhanced-Spectrum-based Map Merging (ESMM) 3.3.1 Overview of the ESMM 3.3.2 Problem Formulation for the ESMM 3.3.3 Preprocessing – Map Thinning 3.3.4 Map Enhancement 3.3.5 Estimation of Map Rotation 3.3.6 Estimation of Map Translations Chapter 4 Map Merging based on Tomographic Correlation 4.1 Overview of the TMM 4.2 Problem Formulation for the TMM 4.3 Extraction of Sinograms by the Radon Transform 4.4 Estimation of a Rotation Angle 4.5 Estimation of X-Y Translations Chapter 5 Experiments 5.1 Experimental Results of the SFMM 5.2 Experimental Results of the ESMM 5.2.1 Results in a Parking Area 5.2.2 Results in a Building Roof 5.3 Experimental Results of the TMM 5.3.1 Results in Indoor Environments 5.3.2 Results in Outdoor Environments 5.3.3 Results with a Public Dataset 5.3.4 Results of Merging More Maps 5.4 Comparison among the Proposed Techniques 5.5 Discussion Chapter 6 Conclusions BibliographyDocto

Intelligent and High-Performance Behavior Design of Autonomous Systems via Learning, Optimization and Control

Nowadays, great societal demands have rapidly boosted the development of autonomous systems that densely interact with humans in many application domains, from manufacturing to transportation and from workplaces to daily lives. The shift from isolated working environments to human-dominated space requires autonomous systems to be empowered to handle not only environmental uncertainties such as external vibrations but also interaction uncertainties arising from human behavior which is in nature probabilistic, causal but not strictly rational, internally hierarchical and socially compliant.This dissertation is concerned with the design of intelligent and high-performance behavior of such autonomous systems, leveraging the strength from control, optimization, learning, and cognitive science. The work consists of two parts. In Part I, the problem of high-level hybrid human-machine behavior design is addressed. The goal is to achieve safe, efficient and human-like interaction with people. A framework based on the theory of mind, utility theories and imitation learning is proposed to efficiently represent and learn the complicated behavior of humans. Built upon that, machine behaviors at three different levels - the perceptual level, the reasoning level, and the action level - are designed via imitation learning, optimization, and online adaptation, allowing the system to interpret, reason and behave as human, particularly when a variety of uncertainties exist. Applications to autonomous driving are considered throughout Part I. Part II is concerned with the design of high-performance low-level individual machine behavior in the presence of model uncertainties and external disturbances. Advanced control laws based on adaptation, iterative learning and the internal structures of uncertainties/disturbances are developed to assure that the high-level interactive behaviors can be reliably executed. Applications on robot manipulators and high-precision motion systems are discussed in this part

Adoption of vehicular ad hoc networking protocols by networked robots

This paper focuses on the utilization of wireless networking in the robotics domain. Many researchers have already equipped their robots with wireless communication capabilities, stimulated by the observation that multi-robot systems tend to have several advantages over their single-robot counterparts. Typically, this integration of wireless communication is tackled in a quite pragmatic manner, only a few authors presented novel Robotic Ad Hoc Network (RANET) protocols that were designed specifically with robotic use cases in mind. This is in sharp contrast with the domain of vehicular ad hoc networks (VANET). This observation is the starting point of this paper. If the results of previous efforts focusing on VANET protocols could be reused in the RANET domain, this could lead to rapid progress in the field of networked robots. To investigate this possibility, this paper provides a thorough overview of the related work in the domain of robotic and vehicular ad hoc networks. Based on this information, an exhaustive list of requirements is defined for both types. It is concluded that the most significant difference lies in the fact that VANET protocols are oriented towards low throughput messaging, while RANET protocols have to support high throughput media streaming as well. Although not always with equal importance, all other defined requirements are valid for both protocols. This leads to the conclusion that cross-fertilization between them is an appealing approach for future RANET research. To support such developments, this paper concludes with the definition of an appropriate working plan

Double elevation: Autonomous weapons and the search for an irreducible law of war

What should be the role of law in response to the spread of artificial intelligence in war? Fuelled by both public and private investment, military technology is accelerating towards increasingly autonomous weapons, as well as the merging of humans and machines. Contrary to much of the contemporary debate, this is not a paradigm change; it is the intensification of a central feature in the relationship between technology and war: Double elevation, above one's enemy and above oneself. Elevation above one's enemy aspires to spatial, moral, and civilizational distance. Elevation above oneself reflects a belief in rational improvement that sees humanity as the cause of inhumanity and de-humanization as our best chance for humanization. The distance of double elevation is served by the mechanization of judgement. To the extent that judgement is seen as reducible to algorithm, law becomes the handmaiden of mechanization. In response, neither a focus on questions of compatibility nor a call for a 'ban on killer robots' help in articulating a meaningful role for law. Instead, I argue that we should turn to a long-standing philosophical critique of artificial intelligence, which highlights not the threat of omniscience, but that of impoverished intelligence. Therefore, if there is to be a meaningful role for law in resisting double elevation, it should be law encompassing subjectivity, emotion and imagination, law irreducible to algorithm, a law of war that appreciates situated judgement in the wielding of violence for the collective

Vision-based Situational Graphs Generating Optimizable 3D Scene Representations

3D scene graphs offer a more efficient representation of the environment by hierarchically organizing diverse semantic entities and the topological relationships among them. Fiducial markers, on the other hand, offer a valuable mechanism for encoding comprehensive information pertaining to environments and the objects within them. In the context of Visual SLAM (VSLAM), especially when the reconstructed maps are enriched with practical semantic information, these markers have the potential to enhance the map by augmenting valuable semantic information and fostering meaningful connections among the semantic objects. In this regard, this paper exploits the potential of fiducial markers to incorporate a VSLAM framework with hierarchical representations that generates optimizable multi-layered vision-based situational graphs. The framework comprises a conventional VSLAM system with low-level feature tracking and mapping capabilities bolstered by the incorporation of a fiducial marker map. The fiducial markers aid in identifying walls and doors in the environment, subsequently establishing meaningful associations with high-level entities, including corridors and rooms. Experimental results are conducted on a real-world dataset collected using various legged robots and benchmarked against a Light Detection And Ranging (LiDAR)-based framework (S-Graphs) as the ground truth. Consequently, our framework not only excels in crafting a richer, multi-layered hierarchical map of the environment but also shows enhancement in robot pose accuracy when contrasted with state-of-the-art methodologies.Comment: 7 pages, 6 figures, 2 table

GUARDIANS final report

Emergencies in industrial warehouses are a major concern for firefghters. The large dimensions together with the development of dense smoke that drastically reduces visibility, represent major challenges. The Guardians robot swarm is designed to assist fire fighters in searching a large warehouse. In this report we discuss the technology developed for a swarm of robots searching and assisting fire fighters. We explain the swarming algorithms which provide the functionality by which the robots react to and follow humans while no communication is required. Next we discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also one of the means to locate the robots and humans. Thus the robot swarm is able to locate itself and provide guidance information to the humans. Together with the re ghters we explored how the robot swarm should feed information back to the human fire fighter. We have designed and experimented with interfaces for presenting swarm based information to human beings

Self-healing radio maps of wireless networks for indoor positioning

Programa Doutoral em Telecomunicações MAP-tele das Universidades do Minho, Aveiro e PortoA Indústria 4.0 está a impulsionar a mudança para novas formas de produção e otimização em tempo real nos espaços industriais que beneficiam das capacidades da Internet of Things (IoT) nomeadamente, a localização de veículos para monitorização e optimização de processos. Normalmente os espaços industriais possuem uma infraestrutura Wi-Fi que pode ser usada para localizar pessoas, bens ou veículos, sendo uma oportunidade para aumentar a produtividade. Os mapas de rádio são importantes para os sistemas de posicionamento baseados em Wi-Fi, porque representam o ambiente de rádio e são usados para estimar uma posição. Os mapas de rádio são constituídos por amostras Wi-Fi recolhidas em posições conhecidas e degradam-se ao longo do tempo devido a vários fatores, por exemplo, efeitos de propagação, adição/remoção de APs, entre outros. O processo de construção do mapa de rádio costuma ser exigente em termos de tempo e recursos humanos, constituindo um desafio considerável. Os veículos, que operam em ambientes industriais podem ser explorados para auxiliar na construção de mapas de rádio, desde que seja possível localizá-los e rastreá-los. O objetivo principal desta tese é desenvolver um sistema de posicionamento para veículos industriais com mapas de rádio auto-regenerativos (capaz de manter os mapas de rádio atualizados). Os veículos são localizados através da fusão sensorial de Wi-Fi com sensores de movimento, que permitem anotar novas amostras Wi-Fi para o mapa de rádio auto-regenerativo. São propostas duas abordagens de fusão sensorial, baseadas em Loose Coupling e Tight Coupling, para a localização dos veículos. A abordagem Tight Coupling inclui uma métrica de confiança para determinar quando é que as amostras de Wi-Fi devem ser anotadas. Deste modo, esta solução não requer calibração nem esforço humano para a construção e manutenção do mapa de rádio. Os resultados obtidos em experiências sugerem que esta solução tem potencial para a IoT e a Indústria 4.0, especialmente em serviços de localização, mas também na monitorização, suporte à navegação autónoma, e interconectividade.Industry 4.0 is driving change for new forms of production and real-time optimization in factories, which benefit from the Industrial Internet of Things (IoT) capabilities to locate industrial vehicles for monitoring, improving safety, and operations. Most industrial environments have a Wi-Fi infrastructure that can be exploited to locate people, assets, or vehicles, providing an opportunity for enhancing productivity and interconnectivity. Radio maps are important for Wi-Fi-based Indoor Position Systems (IPSs) since they represent the radio environment and are used to estimate a position. Radio maps comprise a set of Wi- Fi samples collected at known positions, and degrade over time due to several aspects, e.g., propagation effects, addition/removal of Access Points (APs), among others, hence they should be periodically updated to maintain the IPS performance. The process to build and maintain radio maps is usually time-consuming and demanding in terms of human resources, thus being challenging to perform. Vehicles, commonly present in industrial environments, can be explored to help build and maintain radio maps, as long as it is possible to locate and track them. The main objective of this thesis is to develop an IPS for industrial vehicles with self-healing radio maps (capable of keeping radio maps up to date). Vehicles are tracked using sensor fusion of Wi-Fi with motion sensors, which allows to annotate new Wi-Fi samples to build the self-healing radio maps. Two sensor fusion approaches based on Loose Coupling and Tight Coupling are proposed to track vehicles. The Tight Coupling approach includes a reliability metric to determine when Wi-Fi samples should be annotated. As a result, this solution does not depend on any calibration or human effort to build and maintain the radio map. Results obtained in real-world experiments suggest that this solution has potential for IoT and Industry 4.0, especially in location services, but also in monitoring and analytics, supporting autonomous navigation, and interconnectivity between devices.MAP-Tele Doctoral Programme scientific committee and the FCT (Fundação para a Ciência e Tecnologia) for the PhD grant (PD/BD/137401/2018

Selective combination of visual and thermal imaging for resilient localization in adverse conditions: Day and night, smoke and fire

Long-term autonomy in robotics requires perception systems that are resilient to unusual but realistic conditions that will eventually occur during extended missions. For example, unmanned ground vehicles (UGVs) need to be capable of operating safely in adverse and low-visibility conditions, such as at night or in the presence of smoke. The key to a resilient UGV perception system lies in the use of multiple sensor modalities, e.g., operating at different frequencies of the electromagnetic spectrum, to compensate for the limitations of a single sensor type. In this paper, visual and infrared imaging are combined in a Visual-SLAM algorithm to achieve localization. We propose to evaluate the quality of data provided by each sensor modality prior to data combination. This evaluation is used to discard low-quality data, i.e., data most likely to induce large localization errors. In this way, perceptual failures are anticipated and mitigated. An extensive experimental evaluation is conducted on data sets collected with a UGV in a range of environments and adverse conditions, including the presence of smoke (obstructing the visual camera), fire, extreme heat (saturating the infrared camera), low-light conditions (dusk), and at night with sudden variations of artificial light. A total of 240 trajectory estimates are obtained using five different variations of data sources and data combination strategies in the localization method. In particular, the proposed approach for selective data combination is compared to methods using a single sensor type or combining both modalities without preselection. We show that the proposed framework allows for camera-based localization resilient to a large range of low-visibility conditions