Visual Localization and Mapping in Dynamic and Changing Environments

The real-world deployment of fully autonomous mobile robots depends on a robust SLAM (Simultaneous Localization and Mapping) system, capable of handling dynamic environments, where objects are moving in front of the robot, and changing environments, where objects are moved or replaced after the robot has already mapped the scene. This paper presents Changing-SLAM, a method for robust Visual SLAM in both dynamic and changing environments. This is achieved by using a Bayesian filter combined with a long-term data association algorithm. Also, it employs an efficient algorithm for dynamic keypoints filtering based on object detection that correctly identify features inside the bounding box that are not dynamic, preventing a depletion of features that could cause lost tracks. Furthermore, a new dataset was developed with RGB-D data especially designed for the evaluation of changing environments on an object level, called PUC-USP dataset. Six sequences were created using a mobile robot, an RGB-D camera and a motion capture system. The sequences were designed to capture different scenarios that could lead to a tracking failure or a map corruption. To the best of our knowledge, Changing-SLAM is the first Visual SLAM system that is robust to both dynamic and changing environments, not assuming a given camera pose or a known map, being also able to operate in real time. The proposed method was evaluated using benchmark datasets and compared with other state-of-the-art methods, proving to be highly accurate.Comment: 14 pages, 13 figure

Introdução à Programação de Propósito Geral em Hardware Gráfico

A Unidade de Processamento Gráfico – do inglês “Graphics Processing Unit"(GPU) foi desenvolvida inicialmente como um hardware destinado a aumentar a eficiência e o poder de processamento gráfico para tarefas de renderização. Hoje, a GPU apresenta-se como um hardware de processamento versátil e de alto poder de computação. Tornou-se uma possibilidade real na busca por soluções para processamento em grandes volumes de dados, seja como complemento, seja como alternativa ao uso de CPUs multicore ou de sistemas distribuídos. A utilização da GPU em computações de propósito geral é de especial interesse, uma vez que para diversas aplicações, ainda não existem formulações sequenciais suficientemente rápidas de serem computadas. Este tutorial tem como objetivo permitir ao leitor a identificação de algoritmos e aplicações candidatas à abordagens paralelas em GPU. Com tal finalidade, apresentamos os fundamentos e conceitos envolvidos na programação de propósito genérico utilizando hardware gráfico sem que seja indispensável ao leitor, o conhecimento a priori de sistemas gráficos 3D ou de sistemas paralelos

Detection of Masses in Digital Mammograms using K-means and Support Vector Machine

Breast cancer is a serious public health problem in several countries. Computer Aided Detection/Diagnosis systems (CAD/CADx) have been used with relative success aiding health care professionals. The goal of such systems is contribute on the specialist task aiding in the detection of different types of cancer at an early stage. This work presents a methodology for masses detection on digitized mammograms using the K-means algorithm for image segmentation and co-occurrence matrix to describe the texture of segmented structures. Classification of these structures is accomplished through Support Vector Machines, which separate them in two groups, using shape and texture descriptors: masses and non-masses. The methodology obtained 85% of accuracy

AUTOMATIC METHOD BASED ON PSO-OPTIMIZED VISION-TRANSFORMER FOR GAS DETECTION IN 2D SEISMIC IMAGES

One of the geophysical techniques most frequently utilized in the oil and gas (O\&G) sector for hydrocarbon prospecting is seismic reflection. The seismic reflection technique is essential for an estimate the location and volume of gas accumulations in various onshore fields. However, this technique generates a large amount of data, and its data acquisitions are noisy. Thus it takes a while to analyze and interpret seismic data. Computational techniques based on machine learning have been proposed considering Direct Hydrocarbon Indicators (DHIs) to assist geoscientists in such activities. In this paper, we describe a method to detect gas accumulations based on the Particle Swarm Optimization (PSO) algorithm and the Vision Transformer neural network (ViT). In the best scenario, the proposed method achieved a sensitivity of 88.60%, a specificity of 99.56%  and an accuracy of 99.37%. We present some tests performed on Parnaíba Basin and Netherlands F3-Block fields. Thus, it demonstrates that the proposed method is promising for assisting specialists in gas exploration tasks.DOI: 10.36558/rsc.v12i3.790

An Approach for Construction of Augmented Reality Systems using Natural Markers and Mobile Sensors in Industrial Fields

This paper presents a methodology for the development of augmented reality (AR) visualization applications in industrial scenarios. The proposal presents the use of georreferenced natural markers detected in real time, which enables the construction of AR systems. This use of augmented visualization allows the creation of tools that can aid on-site maintenance activities for operators. AR use makes possible including information about the equipment during a specific procedure. In this work, the detection of natural markers in the scene are based on Haar-like features associated with equipment geolocalization. This approach enable the detection of equipment in multiple user’s viewpoints in the industrial scenario and makes it possible the inclusion of real information about those equipment in real time as AR annotations. In this way, beyond a methodology approach, this paper presents a new way for Power System information visualization in the field that can be used in both for training and for control operations

Visual Field Coordinate Systems in Visual Neurophysiology

Abstract In this work, algorithms have been developed to: 1. Compare visual field coordinate data, presented in different representation systems; 2. Determine the distance in degrees between any two points in the visual field; 3. Predict new coordinates of a given point in the visual field after the rotation of the head, around axes that pass through the nodal point of the eye. Formulas are proposed for the transformation of Polar coordinates into Zenithal Equatorial coordinates and vice versa; of Polar coordinates into Gnomic Equatorial of double meridians and vice versa; and projections of double meridians system into Zenithal Equatorial and vice versa. Using the transformation of polar coordinates into Cartesian coordinates, we can also propose algorithms for rotating the head or the visual field representation system around the dorsoventral, lateral-lateral and anterior-posterior axes, in the mediolateral, dorsoventral and clockwise directions, respectively. In addition, using the concept of the scalar product in linear algebra, we propose new algorithms for calculating the distance between two points and to determine the area of receptive fields in the visual field