Visual-SLAM Classical Framework and Key Techniques: A Review

Abstract

With the significant increase in demand for artificial intelligence, environmental map reconstruction has become a research hotspot for obstacle avoidance navigation, unmanned operations, and virtual reality. The quality of the map plays a vital role in positioning, path planning, and obstacle avoidance. This review starts with the development of SLAM (Simultaneous Localization and Mapping) and proceeds to a review of V-SLAM (Visual-SLAM) from its proposal to the present, with a summary of its historical milestones. In this context, the five parts of the classic V-SLAM framework—visual sensor, visual odometer, backend optimization, loop detection, and mapping—are explained separately. Meanwhile, the details of the latest methods are shown; VI-SLAM (Visual inertial SLAM) is reviewed and extended. The four critical techniques of V-SLAM and its technical difficulties are summarized as feature detection and matching, selection of keyframes, uncertainty technology, and expression of maps. Finally, the development direction and needs of the V-SLAM field are proposed.

Sistema de Análise, Identificação e Mensuração de Ativos Intangíveis

“…A primeira camada, frontend, é definida pela interação do usuário com o sistema. Nela, foram coletados os dados e disponibilizados de forma visual, bem como os resultados obtidos do backend ou dados armazenados no banco de dados, possibilitando ao usuário proceder com a manipulação destes [13].…”

“…A segunda camada, backend, foi responsável por gerenciar toda a interação que o usuário procedeu, com a lógica e armazenamento de dados. Desta forma, no backend estão contidas as regras de negócio, na qual o usuário informa os dados pelo frontend, que por sua vez encaminha para o backend, que processa a informação e retorna para o usuário o dado processado, e se necessário, encaminha para o armazenamento no banco de dados [16,13] Neste projeto o Node.JS foi selecionado como servidor backend pois é um ambiente multiplataforma de desenvolvimento, possuindo suporte a servidores web com base de códigos escritos em JavaScript [17,18] Outro Em cada uma das etapas, sendo elas: Cadastro, Preparação e Identificação, foram implementadas telas de Criação (Figura 1) e Consulta (Figura 2). A consulta por sua vez possibilitou a exclusão de cadastros realizados, e em sua maioria, alteração de informações já cadastradas (Figura 2).…”

SLAM and 3D Semantic Reconstruction Based on the Fusion of Lidar and Monocular Vision

“…Generally, cameras are used in visual SLAM to obtain rich visual information, which performs great advantages and potential in object detection, recognition, and environmental semantic understanding [ 1 ]. However, the quality of the images collected by the cameras is greatly affected by the light, therefore, visual SLAM has low positioning accuracy in scenes with poor illumination.…”

Applying SLAM Algorithm Based on Nonlinear Optimized Monocular Vision and IMU in the Positioning Method of Power Inspection Robot in Complex Environment

“…Pitch and roll values and gyroscope sensor values are estimated based on the combined function of the accelerometer, magnetometer, and gyroscope data from the IMU position sensor and fusion of the accelerometer [15]. On account of the SLAM, Jia et al (2022) studied and proposed the mapping and positioning methods of inspection robots, respectively and further proposed an inspection control method based on iterative learning control (Jia et al, 2022) [16].…”