Keyframe-based monocular SLAM: design, survey, and future directions

Extensive research in the field of monocular SLAM for the past fifteen years has yielded workable systems that found their way into various applications in robotics and augmented reality. Although filter-based monocular SLAM systems were common at some time, the more efficient keyframe-based solutions are becoming the de facto methodology for building a monocular SLAM system. The objective of this paper is threefold: first, the paper serves as a guideline for people seeking to design their own monocular SLAM according to specific environmental constraints. Second, it presents a survey that covers the various keyframe-based monocular SLAM systems in the literature, detailing the components of their implementation, and critically assessing the specific strategies made in each proposed solution. Third, the paper provides insight into the direction of future research in this field, to address the major limitations still facing monocular SLAM; namely, in the issues of illumination changes, initialization, highly dynamic motion, poorly textured scenes, repetitive textures, map maintenance, and failure recovery

A Comparison of Monocular Visual SLAM and Visual Odometry Methods Applied to 3D Reconstruction

This work was supported by the SDAS Research Group (www.sdas-group.com accessed on 16 June 2023).Pure monocular 3D reconstruction is a complex problem that has attracted the research community's interest due to the affordability and availability of RGB sensors. SLAM, VO, and SFM are disciplines formulated to solve the 3D reconstruction problem and estimate the camera's ego-motion; so, many methods have been proposed. However, most of these methods have not been evaluated on large datasets and under various motion patterns, have not been tested under the same metrics, and most of them have not been evaluated following a taxonomy, making their comparison and selection difficult. In this research, we performed a comparison of ten publicly available SLAM and VO methods following a taxonomy, including one method for each category of the primary taxonomy, three machine-learning-based methods, and two updates of the best methods to identify the advantages and limitations of each category of the taxonomy and test whether the addition of machine learning or updates on those methods improved them significantly. Thus, we evaluated each algorithm using the TUM-Mono dataset and benchmark, and we performed an inferential statistical analysis to identify the significant differences through its metrics. The results determined that the sparse-direct methods significantly outperformed the rest of the taxonomy, and fusing them with machine learning techniques significantly enhanced the geometric-based methods' performance from different perspectives.SDAS Research Grou

Odometria visual monocular em robôs para a agricultura com camara(s) com lentes "olho de peixe"

One of the main challenges in robotics is to develop accurate localization methods that achieve acceptable runtime performances.One of the most common approaches is to use Global Navigation Satellite System such as GPS to localize robots.However, satellite signals are not full-time available in some kind of environments.The purpose of this dissertation is to develop a localization system for a ground robot.This robot is inserted in a project called RoMoVi and is intended to perform tasks like crop monitoring and harvesting in steep slope vineyards.This vineyards are localized in the Douro region which are characterized by the presence of high hills.Thus, the context of RoMoVi is not prosperous for the use of GPS-based localization systems.Therefore, the main goal of this work is to create a reliable localization system based on vision techniques and low cost sensors.To do so, a Visual Odometry system will be used.The concept of Visual Odometry is equivalent to wheel odometry but it has the advantage of not suffering from wheel slip which is present in these kind of environments due to the harsh terrain conditions.Here, motion is tracked computing the homogeneous transformation between camera frames, incrementally.However, this approach also presents some open issues.Most of the state of art methods, specially those who present a monocular camera system, don't perform good motion estimations in pure rotations.In some of them, motion even degenerates in these situations.Also, computing the motion scale is a difficult task that is widely investigated in this field.This work is intended to solve these issues.To do so, fisheye lens cameras will be used in order to achieve wide vision field of views

A collaborative monocular visual simultaneous localization and mapping solution to generate a semi-dense 3D map.

The utilization and generation of indoor maps are critical in accurate indoor tracking. Simultaneous Localization and Mapping (SLAM) is one of the main techniques used for such map generation. In SLAM, an agent generates a map of an unknown environment while approximating its own location in it. The prevalence and afford-ability of cameras encourage the use of Monocular Visual SLAM, where a camera is the only sensing device for the SLAM process. In modern applications, multiple mobile agents may be involved in the generation of indoor maps, thus requiring a distributed computational framework. Each agent generates its own local map, which can then be combined with those of other agents into a map covering a larger area. In doing so, they cover a given environment faster than a single agent. Furthermore, they can interact with each other in the same environment, making this framework more practical, especially for collaborative applications such as augmented reality. One of the main challenges of collaborative SLAM is identifying overlapping maps, especially when the relative starting positions of the agents are unknown. We propose a system comprised of multiple monocular agents with unknown relative starting positions to generate a semi-dense global map of the environment

Visual SLAM algorithms: a survey from 2010 to 2016

SLAM is an abbreviation for simultaneous localization and mapping, which is a technique for estimating sensor motion and reconstructing structure in an unknown environment. Especially, Simultaneous Localization and Mapping (SLAM) using cameras is referred to as visual SLAM (vSLAM) because it is based on visual information only. vSLAM can be used as a fundamental technology for various types of applications and has been discussed in the field of computer vision, augmented reality, and robotics in the literature. This paper aims to categorize and summarize recent vSLAM algorithms proposed in different research communities from both technical and historical points of views. Especially, we focus on vSLAM algorithms proposed mainly from 2010 to 2016 because major advance occurred in that period. The technical categories are summarized as follows: feature-based, direct, and RGB-D camera-based approaches

Real-Time Accurate Visual SLAM with Place Recognition

El problema de localización y construcción simultánea de mapas (del inglés Simultaneous Localization and Mapping, abreviado SLAM) consiste en localizar un sensor en un mapa que se construye en línea. La tecnología de SLAM hace posible la localización de un robot en un entorno desconocido para él, procesando la información de sus sensores de a bordo y por tanto sin depender de infraestructuras externas. Un mapa permite localizarse en todo momento sin acumular deriva, a diferencia de una odometría donde se integran movimientos incrementales. Este tipo de tecnología es crítica para la navegación de robots de servicio y vehículos autónomos, o para la localización del usuario en aplicaciones de realidad aumentada o virtual. La principal contribución de esta tesis es ORB-SLAM, un sistema de SLAM monocular basado en características que trabaja en tiempo real en ambientes pequeños y grandes, de interior y exterior. El sistema es robusto a elementos dinámicos en la escena, permite cerrar bucles y relocalizar la cámara incluso si el punto de vista ha cambiado significativamente, e incluye un método de inicialización completamente automático. ORB-SLAM es actualmente la solución más completa, precisa y fiable de SLAM monocular empleando una cámara como único sensor. El sistema, estando basado en características y ajuste de haces, ha demostrado una precisión y robustez sin precedentes en secuencias públicas estándar.Adicionalmente se ha extendido ORB-SLAM para reconstruir el entorno de forma semi-densa. Nuestra solución desacopla la reconstrucción semi-densa de la estimación de la trayectoria de la cámara, lo que resulta en un sistema que combina la precisión y robustez del SLAM basado en características con las reconstrucciones más completas de los métodos directos. Además se ha extendido la solución monocular para aprovechar la información de cámaras estéreo, RGB-D y sensores inerciales, obteniendo precisiones superiores a otras soluciones del estado del arte. Con el fin de contribuir a la comunidad científica, hemos hecho libre el código de una implementación de nuestra solución de SLAM para cámaras monoculares, estéreo y RGB-D, siendo la primera solución de código libre capaz de funcionar con estos tres tipos de cámara. Bibliografía:R. Mur-Artal and J. D. Tardós.Fast Relocalisation and Loop Closing in Keyframe-Based SLAM.IEEE International Conference on Robotics and Automation (ICRA). Hong Kong, China, June 2014.R. Mur-Artal and J. D. Tardós.ORB-SLAM: Tracking and Mapping Recognizable Features.RSS Workshop on Multi VIew Geometry in RObotics (MVIGRO). Berkeley, USA, July 2014. R. Mur-Artal and J. D. Tardós.Probabilistic Semi-Dense Mapping from Highly Accurate Feature-Based Monocular SLAM.Robotics: Science and Systems (RSS). Rome, Italy, July 2015.R. Mur-Artal, J. M. M. Montiel and J. D. Tardós.ORB-SLAM: A Versatile and Accurate Monocular SLAM System.IEEE Transactions on Robotics, vol. 31, no. 5, pp. 1147-1163, October 2015.(2015 IEEE Transactions on Robotics Best Paper Award).R. Mur-Artal, and J. D. Tardós.Visual-Inertial Monocular SLAM with Map Reuse.IEEE Robotics and Automation Letters, vol. 2, no. 2, pp. 796-803, April 2017. (to be presented at ICRA 17).R.Mur-Artal, and J. D. Tardós. ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras.ArXiv preprint arXiv:1610.06475, 2016. (under Review).<br /