Monocular visual SLAM, visual odometry, and structure from motion methods applied to 3D reconstruction: A comprehensive survey.

Monocular Simultaneous Localization and Mapping (SLAM), Visual Odometry (VO), and Structure from Motion (SFM) are techniques that have emerged recently to address the problem of reconstructing objects or environments using monocular cameras. Monocular pure visual techniques have become attractive solutions for 3D reconstruction tasks due to their affordability, lightweight, easy deployment, good outdoor performance, and availability in most handheld devices without requiring additional input devices. In this work, we comprehensively overview the SLAM, VO, and SFM solutions for the 3D reconstruction problem that uses a monocular RGB camera as the only source of information to gather basic knowledge of this ill-posed problem and classify the existing techniques following a taxonomy. To achieve this goal, we extended the existing taxonomy to cover all the current classifications in the literature, comprising classic, machine learning, direct, indirect, dense, and sparse methods. We performed a detailed overview of 42 methods, considering 18 classic and 24 machine learning methods according to the ten categories defined in our extended taxonomy, comprehensively systematizing their algorithms and providing their basic formulations. Relevant information about each algorithm was summarized in nine criteria for classic methods and eleven criteria for machine learning methods to provide the reader with decision components to implement, select or design a 3D reconstruction system. Finally, an analysis of the temporal evolution of each category was performed, which determined that the classical-sparse-indirect and classical-dense-indirect categories have been the most accepted solutions to the monocular 3D reconstruction problem over the last 18 years.

SLAM family-mediated crosstalk between tumor and immune cells in the tumor microenvironment: a promising biomarker and a potential therapeutic target for immune checkpoint therapies.

Immune cells infiltrating the tumor microenvironment are physiologically important in controlling cancers. However, emerging studies have shown that cancer cells can evade immune surveillance and establish a balance in which these immune cells support tumor progression and therapeutic resistance. The signaling lymphocytic activation molecule family members have been recognized as mediators of tumor microenvironment interactions, and a promising therapeutic target for cancer immunotherapy. This review is focused on the role of SLAM family in tumor and immune cell interactions and discusses how such crosstalk affects tumor behavior. This will shed insight into the next step toward improving cancer immunotherapy.

Advancing Global Pose Refinement: A Linear, Parameter-Free Model for Closed Circuits via Quaternion Interpolation.

Global pose refinement is a significant challenge within Simultaneous Localization and Mapping (SLAM) frameworks. For LIDAR-based SLAM systems, pose refinement is integral to correcting drift caused by the successive registration of 3D point clouds collected by the sensor. A divergence between the actual and calculated platform paths characterizes this error. In response to this challenge, we propose a linear, parameter-free model that uses a closed circuit for global trajectory corrections. Our model maps rotations to quaternions and uses Spherical Linear Interpolation (SLERP) for transitions between them. The intervals are established by the constraint set by the Least Squares (LS) method on rotation closure and are proportional to the circuit's size. Translations are globally adjusted in a distinct linear phase. Additionally, we suggest a coarse-to-fine pairwise registration method, integrating Fast Global Registration and Generalized ICP with multiscale sampling and filtering. The proposed approach is tested on three varied datasets of point clouds, including Mobile Laser Scanners and Terrestrial Laser Scanners. These diverse datasets affirm the model's effectiveness in 3D pose estimation, with substantial pose differences and efficient pose optimization in larger circuits.

NeoSLAM: Long-Term SLAM Using Computational Models of the Brain.

Simultaneous Localization and Mapping (SLAM) is a fundamental problem in the field of robotics, enabling autonomous robots to navigate and create maps of unknown environments. Nevertheless, the SLAM methods that use cameras face problems in maintaining accurate localization over extended periods across various challenging conditions and scenarios. Following advances in neuroscience, we propose NeoSLAM, a novel long-term visual SLAM, which uses computational models of the brain to deal with this problem. Inspired by the human neocortex, NeoSLAM is based on a hierarchical temporal memory model that has the potential to identify temporal sequences of spatial patterns using sparse distributed representations. Being known to have a high representational capacity and high tolerance to noise, sparse distributed representations have several properties, enabling the development of a novel neuroscience-based loop-closure detector that allows for real-time performance, especially in resource-constrained robotic systems. The proposed method has been thoroughly evaluated in terms of environmental complexity by using a wheeled robot deployed in the field and demonstrated that the accuracy of loop-closure detection was improved compared with the traditional RatSLAM system.

A New Method for Classifying Scenes for Simultaneous Localization and Mapping Using the Boundary Object Function Descriptor on RGB-D Points.

Scene classification in autonomous navigation is a highly complex task due to variations, such as light conditions and dynamic objects, in the inspected scenes; it is also a challenge for small-factor computers to run modern and highly demanding algorithms. In this contribution, we introduce a novel method for classifying scenes in simultaneous localization and mapping (SLAM) using the boundary object function (BOF) descriptor on RGB-D points. Our method aims to reduce complexity with almost no performance cost. All the BOF-based descriptors from each object in a scene are combined to define the scene class. Instead of traditional image classification methods such as ORB or SIFT, we use the BOF descriptor to classify scenes. Through an RGB-D camera, we capture points and adjust them onto layers than are perpendicular to the camera plane. From each plane, we extract the boundaries of objects such as furniture, ceilings, walls, or doors. The extracted features compose a bag of visual words classified by a support vector machine. The proposed method achieves almost the same accuracy in scene classification as a SIFT-based algorithm and is 2.38× faster. The experimental results demonstrate the effectiveness of the proposed method in terms of accuracy and robustness for the 7-Scenes and SUNRGBD datasets.

Robust Localization for Underground Mining Vehicles: An Application in a Room and Pillar Mine.

Most autonomous navigation systems used in underground mining vehicles such as load-haul-dump (LHD) vehicles and trucks use 2D light detection and ranging (LIDAR) sensors and 2D representations/maps of the environment. In this article, we propose the use of 3D LIDARs and existing 3D simultaneous localization and mapping (SLAM) jointly with 2D mapping methods to produce or update 2D grid maps of underground tunnels that may have significant elevation changes. Existing mapping methods that only use 2D LIDARs are shown to fail to produce accurate 2D grid maps of the environment. These maps can be used for robust localization and navigation in different mine types (e.g., sublevel stoping, block/panel caving, room and pillar), using only 2D LIDAR sensors. The proposed methodology was tested in the Werra Potash Mine located at Philippsthal, Germany, under real operational conditions. The obtained results show that the enhanced 2D map-building method produces a superior mapping performance compared with a 2D map generated without the use of the 3D LIDAR-based mapping solution. The 2D map generated enables robust 2D localization, which was tested during the operation of an autonomous LHD, performing autonomous navigation and autonomous loading over extended periods of time.

xRatSLAM: An Extensible RatSLAM Computational Framework.

Simultaneous localization and mapping (SLAM) refers to techniques for autonomously constructing a map of an unknown environment while, at the same time, locating the robot in this map. RatSLAM, a prevalent method, is based on the navigation system found in rodent brains. It has served as a base algorithm for other bioinspired approaches, and its implementation has been extended to incorporate new features. This work proposes xRatSLAM: an extensible, parallel, open-source framework applicable for developing and testing new RatSLAM variations. Tests were carried out to evaluate and validate the proposed framework, allowing the comparison of xRatSLAM with OpenRatSLAM and assessing the impact of replacing framework components. The results provide evidence that the maps produced by xRatSLAM are similar to those produced by OpenRatSLAM when they are fed with the same input parameters, which is a positive result, and that implemented modules can be easily changed without impacting other parts of the framework.

Technical Development of the CeCi Social Robot.

This research presents the technical considerations for implementing the CeCi (Computer Electronic Communication Interface) social robot. In this case, this robot responds to the need to achieve technological development in an emerging country with the aim of social impact and social interaction. There are two problems with the social robots currently on the market, which are the main focus of this research. First, their costs are not affordable for companies, universities, or individuals in emerging countries. The second is that their design is exclusively oriented to the functional part with a vision inherent to the engineers who create them without considering the vision, preferences, or requirements of the end users, especially for their social interaction. This last reason ends causing an aversion to the use of this type of robot. In response to the issues raised, a low-cost prototype is proposed, starting from a commercial platform for research development and using open source code. The robot design presented here is centered on the criteria and preferences of the end user, prioritizing acceptability for social interaction. This article details the selection process and hardware capabilities of the robot. Moreover, a programming section is provided to introduce the different software packages used and adapted for the social interaction, the main functions implemented, as well as the new and original part of the proposal. Finally, a list of applications currently developed with the robot and possible applications for future research are discussed.

2D SLAM Algorithms Characterization, Calibration, and Comparison Considering Pose Error, Map Accuracy as Well as CPU and Memory Usage.

The present work proposes a method to characterize, calibrate, and compare, any 2D SLAM algorithm, providing strong statistical evidence, based on descriptive and inferential statistics to bring confidence levels about overall behavior of the algorithms and their comparisons. This work focuses on characterize, calibrate, and compare Cartographer, Gmapping, HECTOR-SLAM, KARTO-SLAM, and RTAB-Map SLAM algorithms. There were four metrics in place: pose error, map accuracy, CPU usage, and memory usage; from these four metrics, to characterize them, Plackett-Burman and factorial experiments were performed, and enhancement after characterization and calibration was granted using hypothesis tests, in addition to the central limit theorem.

Passive Landmark Geometry Optimization and Evaluation for Reliable Autonomous Navigation in Mining Tunnels Using 2D Lidars.

Autonomous navigation in mining tunnels is challenging due to the lack of satellite positioning signals and visible natural landmarks that could be exploited by ranging systems. Solutions requiring stable power feeds for locating beacons and transmitters are not accepted because of accidental damage risks and safety requirements. Hence, this work presents an autonomous navigation approach based on artificial passive landmarks, whose geometry has been optimized in order to ensure drift-free localization of mobile units typically equipped with lidar scanners. The main contribution of the approach lies in the design and optimization of the landmarks that, combined with scan matching techniques, provide a reliable pose estimation in modern smoothly bored mining tunnels. A genetic algorithm is employed to optimize the landmarks' geometry and positioning, thus preventing that the localization problem becomes ill-posed. The proposed approach is validated both in simulation and throughout a series of experiments with an industrial skid-steer CAT 262C robotic excavator, showing the feasibility of the approach with inexpensive passive and low-maintenance landmarks. The results show that the optimized triangular and symmetrical landmarks improve the positioning accuracy by 87.5% per 100 m traveled compared to the accuracy without landmarks. The role of optimized artificial landmarks in the context of modern smoothly bored mining tunnels should not be understated. The results confirm that without the optimized landmarks, the localization error accumulates due to odometry drift and that, contrary to the general intuition or belief, natural tunnel features alone are not sufficient for unambiguous localization. Therefore, the proposed approach ensures grid-based SLAM techniques can be implemented to successfully navigate in smoothly bored mining tunnels.

Canine Morbillivirus from Colombian Lineage Exhibits In Silico and In Vitro Potential to Infect Human Cells.

Canine morbillivirus (CDV) is a viral agent that infects domestic dogs and a vast array of wildlife species. It belongs to the Paramyxoviridae family, genus Morbillivirus, which is shared with the Measles virus (MeV). Both viruses employ orthologous cellular receptors, SLAM in mononuclear cells and Nectin-4 in epithelial cells, to enter the cells. Although CDV and MeV hemagglutinin (H) have similar functions in viral pathogenesis and cell tropism, the potential interaction of CDV-H protein with human cellular receptors is still uncertain. Considering that CDV is classified as a multi-host pathogen, the potential risk of CDV transmission to humans has not been fully discarded. In this study, we aimed to evaluate both in silico and in vitro, whether there is a cross-species transmission potential from CDV to humans. To accomplish this, the CDV-H protein belonging to the Colombian lineage was modelled. After model validations, molecular docking and molecular dynamics simulations were carried out between Colombian CDV-H protein and canine and human cellular receptors to determine different aspects of the protein-protein interactions. Moreover, cell lines expressing orthologous cellular receptors, with both reference and wild-type CDV strains, were conducted to determine the CDV cross-species transmission potential from an in vitro model. This in silico and in vitro approach suggests the possibility that CDV interacts with ortholog human SLAM (hSLAM) and human Nectin-4 receptors to infect human cell lines, which could imply a potential cross-species transmission of CDV from dogs to humans.

A Hybrid Visual-Based SLAM Architecture: Local Filter-Based SLAM with KeyFrame-Based Global Mapping.

This work presents a hybrid visual-based SLAM architecture that aims to take advantage of the strengths of each of the two main methodologies currently available for implementing visual-based SLAM systems, while at the same time minimizing some of their drawbacks. The main idea is to implement a local SLAM process using a filter-based technique, and enable the tasks of building and maintaining a consistent global map of the environment, including the loop closure problem, to use the processes implemented using optimization-based techniques. Different variants of visual-based SLAM systems can be implemented using the proposed architecture. This work also presents the implementation case of a full monocular-based SLAM system for unmanned aerial vehicles that integrates additional sensory inputs. Experiments using real data obtained from the sensors of a quadrotor are presented to validate the feasibility of the proposed approach.

O que a juventude negra do slam tem a dizer para a psicologia social? / What do slam black youth have to say to social psychology? / ¿qué tienen que decir la juventud negra del slam a la psicología social?

Resumo Este trabalho discute como as poesias no slam tensionam a sociedade no contexto do racismo em suas diversas dimensões. As rodas de slam são um espaço onde slammers expõem manifestos poéticos sobre racismo e formas de opressão e onde a juventude negra se inscreve no mundo por meio de sua narrativa, interagindo com seus iguais e produzindo relações que fortalecem, legitimam e valorizam suas vivências. A partir da abordagem afrocêntrica e afrorreferenciada, tomamos poesias de slammers como materialidade para problematizar o lugar que a temática racial ocupa na Psicologia Social, pensando o social como um campo problemático construído e produzido a partir de diferentes práticas humanas. Com isso, podemos abrir na roda da psicologia social uma fenda que desloca a produção de conhecimento a partir da pluriversalidade, ou seja, as possibilidades de as rodas de slam convidarem a psicologia social a compor teorias localizadas fora do círculo acadêmico ocidentocêntrico.

Resumen Este trabajo analiza cómo la poesía en slam tensiona a la sociedad en el contexto del racismo en sus diversas dimensiones. Las ruedas de slam son espacios donde los slammers exponen manifiestos poéticos sobre el racismo y las formas de opresión y donde los jóvenes negros se inscriben en el mundo a través de su narrativa, interactuando con sus pares y produciendo relaciones que fortalecen, legitiman y valoran sus vivencias. Desde un enfoque afrocéntrico y afroreferenciado, tomamos la poesía de slammers como materialidad para problematizar el lugar que ocupan los temas raciales en la Psicología Social, pensando en lo social como un campo problemático construido y producido a partir de diferentes prácticas humanas. Con esto, podemos abrir una brecha en la rueda de la psicología social que desplaza la producción de conocimiento de la pluriversalidad, es decir, las posibilidades de ruedas de slam para invitar a la psicología social a componer teorías ubicadas fuera del círculo académico occidental-céntrico.

Abstract This paper discusses how poetry in slam tensions society in the context of racism in its various dimensions. The slam wheels are spaces where slammers expose poetic manifestos about racism and other forms of oppression, and where black youth are inscribed in the world through their narratives, interacting with their peers and producing relations that strengthen, legitimize and value their experiences. From an Afrocentric and African-referenced approach, we take slammers poetry as materiality to problematize the place that racial themes occupy in Social Psychology, thinking of the social as a problematic field constructed and produced from different human practices. With this, we can open a crack in the circle of social psychology that displaces the production of knowledge from pluriversality, that is, the possibilities of slam wheels to invite social psychology to compose theories outside the Western-centered academic circle.

Simultaneous Indoor Pedestrian Localization and House Mapping Based on Inertial Measurement Unit and Bluetooth Low-Energy Beacon Data.

Indoor location estimation is crucial to provide context-based assistance in home environments. In this study, a method for simultaneous indoor pedestrian localization and house mapping is proposed and evaluated. The method fuses a person's movement data from an Inertial Measurement Unit (IMU) with proximity and activity-related data from Bluetooth Low-Energy (BLE) beacons deployed in the indoor environment. The person's and beacons' localization is performed simultaneously using a combination of particle and Kalman Filters. We evaluated the method using data from eight participants who performed different activities in an indoor environment. As a result, the average participant's localization error was 1.05 ± 0.44 m, and the average beacons' localization error was 0.82 ± 0.24 m. The proposed method is able to construct a map of the indoor environment by localizing the BLE beacons and simultaneously locating the person. The results obtained demonstrate that the proposed method could point to a promising roadmap towards the development of simultaneous localization and home mapping system based only on one IMU and a few BLE beacons. To the best of our knowledge, this is the first method that includes the beacons' data movement as activity-related events in a method for pedestrian Simultaneous Localization and Mapping (SLAM).

Monocular Visual SLAM Based on a Cooperative UAV-Target System.

To obtain autonomy in applications that involve Unmanned Aerial Vehicles (UAVs), the capacity of self-location and perception of the operational environment is a fundamental requirement. To this effect, GPS represents the typical solution for determining the position of a UAV operating in outdoor and open environments. On the other hand, GPS cannot be a reliable solution for a different kind of environments like cluttered and indoor ones. In this scenario, a good alternative is represented by the monocular SLAM (Simultaneous Localization and Mapping) methods. A monocular SLAM system allows a UAV to operate in a priori unknown environment using an onboard camera to simultaneously build a map of its surroundings while at the same time locates itself respect to this map. So, given the problem of an aerial robot that must follow a free-moving cooperative target in a GPS denied environment, this work presents a monocular-based SLAM approach for cooperative UAV-Target systems that addresses the state estimation problem of (i) the UAV position and velocity, (ii) the target position and velocity, (iii) the landmarks positions (map). The proposed monocular SLAM system incorporates altitude measurements obtained from an altimeter. In this case, an observability analysis is carried out to show that the observability properties of the system are improved by incorporating altitude measurements. Furthermore, a novel technique to estimate the approximate depth of the new visual landmarks is proposed, which takes advantage of the cooperative target. Additionally, a control system is proposed for maintaining a stable flight formation of the UAV with respect to the target. In this case, the stability of control laws is proved using the Lyapunov theory. The experimental results obtained from real data as well as the results obtained from computer simulations show that the proposed scheme can provide good performance.

Identification of a murine cell line that distinguishes virulent from attenuated isolates of the morbillivirus Peste des Petits Ruminants, a promising tool for virulence studies.

Comprehensive pathogenesis studies on Peste des Petits Ruminants virus (PPRV) have been delayed so far by the absence of a small animal model reproducing the disease or an in vitro biological system revealing virulence differences. In this study, a mouse 10T1/2 cell line has been identified as presenting different susceptibility to virulent and attenuated PPRV strains. As evidenced by immunofluorescence test and RT-PCR, both virulent and attenuated PPR viruses penetrated and initiated the replication cycle in 10T1/2 cells, independently of the presence of the SLAM goat receptor. However, only virulent strains successfully completed their replication cycle while the vaccine strains did not. Since 10T1/2 cells are interferon-producing cells, the role of the type I interferon (type I IFN) response on this differentiated replication between virulent and attenuated strains was verified by stimulation or repression. Modulation of the type I IFN response did not improve the replication of the vaccine strains, indicating that other cell factor(s) not yet established may hinder the replication of attenuated PPRV in 10T1/2. This 10T1/2 cell line can be proposed as a new in vitro tool for PPRV-host interaction and virulence studies.

Escrever como um ato de resistência: uma escrita rizoma / Writing as an act of resistance: a rhizome writing / Escribir como un acto de resistencia: una escritura rizoma / Écrire comme un acte de résistance: une écriture rhizome

O movimento desta escrita acompanha o movimento de produção de tese intitulada: Novos coletivos de resistência em produção: o que pode um corpo político poético? É uma escrita rizoma. Portanto, uma escrita resistência e não sobre resistência. Anuncia os recolhimentos de campo, as transmutações e os encontros com os coletivos de resistência que se conectam às produções de mulheres poetas, pretas e moradoras da periferia da cidade de Belém-PA. Assim, é uma batalha, uma partilha e uma saúde. O artigo parte do encontro entre a pesquisadora, e poeta, da Faculdade de Saúde Pública da Universidade de São Paulo (USP), sua orientadora e militante do SUS, e as mulheres, manas, e poetas, de Belém e Manaus - AM, que fazem parte do coletivo Slam Dandaras do Norte, e que protagonizam encontros-batalhas de poesia, para produzir encontros poéticos, manifestos e atos que potencializem suas existências, demandas políticas, alegrias e produção de si.

The movement of this writing accompanies the thesis production movement entitled: New resistance groups being produced: what can a poetic political body? It is a rhizome writing. So a writing resistance, and not about resistance. It announces the field recollections, the transmutations and the encounters, with the resistance collectives that connect to the poetic productions of women poets, blacks and dwellers of the periphery of the city of Belém-PA. So it's a battle, a sharing and a health. The article starts with the cartographic experimentation between the researcher and poet, the University of São Paulo's School of Public Health (USP), its director and militant of the SUS, and the women, dudes, and poets of Belém and Manaus-AM, who are part of the collective Slam Dandaras do Norte, who play poetry encounters, to produce poetic encounters, manifestos and acts that potentiate their existences, political demands, joys and self-production.

El movimiento de esa escritura acompana el movimiento de producción de tesis titulada: Nuevos colectivos de resistencia en producción: iqué puede un cuerpo político poético? Es una escritura rizoma. Por lo tanto, una escritura resistencia y no sobre resistencia. Anuncia lo que há sido recogido en el campo, las transmutaciones y los encuentros con los colectivos de resistencia que se conectan a las producciones poéticas de mujeres poetas, negras y moradoras de la periferia de la ciudad de Belém-PA. Así, es una batalla, un compartir y una salud. El artículo parte de la experimentación cartográfica entre la investigadora y poeta, de la Facultad de Salud Pública de la Universidad de São Paulo (USP), su orientadora y militante del SUS, y las mujeres, manas, y poetas, de Belém y Manaus-AM, que forman parte del colectivo Slam Dandaras del Norte, y que protagonizan encuentros-batallas de poesía, para producir encuentros poéticos, manifiestos y actos que potencien sus existencias, demandas políticas, alegrias y producción de sí.

Le mouvement de cette écriture accompagne le mouvement de production de thèse intitulé: Nouveaux collectif de résistance en production: que peut un corps politique poétique? C'est un rhizome écrit. Donc, une résistance à l 'écriture et non une résistance excessive. Annonce le rassemblements sur le terrain, transmutations et rencontres avec des groupes de résistance qui se connectent aux productions de poètes, femmes noires et femmes vivant à la périphérie de ville de Belém-PA. C'est donc une bataille, un partage et une santé. L'article commence à partir du rencontre entre le chercheur et poète de la Faculté de Santé Publique de L'université de São Paulo (USP), son directeur et militant du SUS, et femmes, manas et poètes, de Belém et de Manaus-AM, qui font partie du collectif Slam Dandaras do Norte, et qui rencontres poétiques - batailles de poésie, pour produire des rencontres poétiques, des manifestes et des actes qui potentialisent leur existence, leurs revendications politiques, leurs joies et leur production.

δ-Generalized Labeled Multi-Bernoulli Simultaneous Localization and Mapping with an Optimal Kernel-Based Particle Filtering Approach.

Under realistic environmental conditions, heuristic-based data association and map management routines often result in divergent map and trajectory estimates in robotic Simultaneous Localization And Mapping (SLAM). To address these issues, SLAM solutions have been proposed based on the Random Finite Set (RFS) framework, which models the map and measurements such that the usual requirements of external data association routines and map management heuristics can be circumvented and realistic sensor detection uncertainty can be taken into account. Rao-Blackwellized particle filter (RBPF)-based RFS SLAM solutions have been demonstrated using the Probability Hypothesis Density (PHD) filter and subsequently the Labeled Multi-Bernoulli (LMB) filter. In multi-target tracking, the LMB filter, which was introduced as an efficient approximation to the computationally expensive δ -Generalized LMB ( δ -GLMB) filter, converts its representation of an LMB distribution to δ -GLMB form during the measurement update step. This not only results in a loss of information yielding inferior results (compared to the δ -GLMB filter) but also fails to take computational advantages in parallelized implementations possible with RBPF-based SLAM algorithms. Similar to state-of-the-art random vector-valued RBPF solutions such as FastSLAM and MH-FastSLAM, the performances of all RBPF-based SLAM algorithms based on the RFS framework also diverge from ground truth over time due to random sampling approaches, which only rely on control noise variance. Further, the methods lose particle diversity and diverge over time as a result of particle degeneracy. To alleviate this problem and further improve the quality of map estimates, a SLAM solution using an optimal kernel-based particle filter combined with an efficient variant of the δ -GLMB filter ( δ -GLMB-SLAM) is presented. The performance of the proposed δ -GLMB-SLAM algorithm, referred to as δ -GLMB-SLAM2.0, was demonstrated using simulated datasets and a section of the publicly available KITTI dataset. The results suggest that even with a limited number of particles, δ -GLMB-SLAM2.0 outperforms state-of-the-art RBPF-based RFS SLAM algorithms.

Visual-Based SLAM Configurations for Cooperative Multi-UAV Systems with a Lead Agent: An Observability-Based Approach.

In this work, the problem of the cooperative visual-based SLAM for the class of multi-UA systems that integrates a lead agent has been addressed. In these kinds of systems, a team of aerial robots flying in formation must follow a dynamic lead agent, which can be another aerial robot, vehicle or even a human. A fundamental problem that must be addressed for these kinds of systems has to do with the estimation of the states of the aerial robots as well as the state of the lead agent. In this work, the use of a cooperative visual-based SLAM approach is studied in order to solve the above problem. In this case, three different system configurations are proposed and investigated by means of an intensive nonlinear observability analysis. In addition, a high-level control scheme is proposed that allows to control the formation of the UAVs with respect to the lead agent. In this work, several theoretical results are obtained, together with an extensive set of computer simulations which are presented in order to numerically validate the proposal and to show that it can perform well under different circumstances (e.g., GPS-challenging environments). That is, the proposed method is able to operate robustly under many conditions providing a good position estimation of the aerial vehicles and the lead agent as well.

Effect of the signaling lymphocytic activation molecule (SLAM) in the modulation of T cells in immune response to Leishmania braziliensis in vitro / Efeito da molécula de sinalização para ativação linfocítica (SLAM) na modulação de células T na resposta imune à Leishmania braziliensis in vitro

Introduction: Signaling lymphocyte activation molecule (SLAM) is a self-ligand receptor on the surface of activated T- and B-lymphocytes, macrophages, and DC. Studies have shown PBMC from healthy individuals exposed to Leishmania differ in IFN-γ production. Objective: We investigated the role of SLAM signaling pathway in PMBC from high (HP) and low (LP) IFN-γ producers exposed to L. braziliensis in vitro. Methods: PBMC from 43 healthy individuals were cultured with or without antigen, α-SLAM, rIL-12 and rIFN-γ. The cytokines production was evaluated by ELISA, and SLAM expression by flow cytometry. Results: L. braziliensis associated with rIFN-γ or rIL-12 reduced early SLAM but did not modify this response later in HP. α-SLAM did not alter CD3+SLAM+ expression, and not affected IFN-γ and IL-13 production, in both groups, but increased significantly IL-10 in HP. Leishmania associated with α-SLAM and rIL-12 increased IFN-γ in LP, as well as IL-13 in HP. LP group presented low IFN-γ and IL-13 production, and low SLAM expression. Conclusion: Collectively, these findings suggest that when PBMC from healthy individuals are sensitized with L. braziliensis in vitro, SLAM acts in modulating Th1 response in HP individuals and induces a condition of immunosuppression in LP individuals. (AU)

Introdução: A molécula de sinalização para ativação linfocítica (SLAM) é um receptor autoligante na superfície de linfócitos T e B ativados, macrófagos e DC. Estudos têm mostrado que PBMC de indivíduos saudáveis expostos à Leishmania diferem na produção de IFN-γ. Objetivo: Nós investigamos o papel da via de sinalização de SLAM em PMBC de altos produtores de IFN-γ (AP) e baixos (BP) expostos à L. braziliensis in vitro. Métodos: PBMC de 43 indivíduos saudáveis foram cultivadas com ou sem antígeno, α-SLAM, rIL-12 e rIFN-γ. Foi avaliada a produção de citocinas por ELISA e expressão de SLAM por citometria de fluxo. Resultados: L. braziliensis associado a rIFN-γ ou rIL-12 reduziu a expressão inicial de SLAM, mas não modificou esta resposta mais tarde em AP. α-SLAM não alterou a expressão de CD3+SLAM+, e não afetou a produção de IFN-γ e IL-13, em ambos os grupos, mas aumentou significativamente IL-10 em AP. Leishmania associada a α-SLAM e rIL-12 aumentou IFN-γ em BP, assim como IL-13 em AP. BP apresentaram baixa produção de IFN-γ e IL-13 e baixa expressão de SLAM. Conclusão: Coletivamente, esses achados sugerem que quando PBMC de indivíduos saudáveis são sensibilizados por L. braziliensis in vitro, SLAM atua na modulação da resposta Th1 em indivíduos AP e induz uma condição de imunossupressão em indivíduos BP. (AU)