An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor

This paper presents a novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system with loop-closing and relocalization capabilities targeted for the underwater domain. Our previous work, SVIn, augmented the state-of-the-art visual-inertial state estimation package OKVIS to accommodate acoustic data from sonar in a non-linear optimization-based framework. This paper addresses drift and loss of localization -- one of the main problems affecting other packages in underwater domain -- by providing the following main contributions: a robust initialization method to refine scale using depth measurements, a fast preprocessing step to enhance the image quality, and a real-time loop-closing and relocalization method using bag of words (BoW). An additional contribution is the addition of depth measurements from a pressure sensor to the tightly-coupled optimization formulation. Experimental results on datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle from challenging underwater environments with poor visibility demonstrate performance never achieved before in terms of accuracy and robustness

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 /

UcoSLAM: Simultaneous Localization and Mapping by Fusion of KeyPoints and Squared Planar Markers

This paper proposes a novel approach for Simultaneous Localization and Mapping by fusing natural and artificial landmarks. Most of the SLAM approaches use natural landmarks (such as keypoints). However, they are unstable over time, repetitive in many cases or insufficient for a robust tracking (e.g. in indoor buildings). On the other hand, other approaches have employed artificial landmarks (such as squared fiducial markers) placed in the environment to help tracking and relocalization. We propose a method that integrates both approaches in order to achieve long-term robust tracking in many scenarios. Our method has been compared to the start-of-the-art methods ORB-SLAM2 and LDSO in the public dataset Kitti, Euroc-MAV, TUM and SPM, obtaining better precision, robustness and speed. Our tests also show that the combination of markers and keypoints achieves better accuracy than each one of them independently.Comment: Paper submitted to Pattern Recognitio

ATDN vSLAM: An all-through Deep Learning-Based Solution for Visual Simultaneous Localization and Mapping

In this paper, a novel solution is introduced for visual Simultaneous Localization and Mapping (vSLAM) that is built up of Deep Learning components. The proposed architecture is a highly modular framework in which each component offers state of the art results in their respective fields of vision-based deep learning solutions. The paper shows that with the synergic integration of these individual building blocks, a functioning and efficient all-through deep neural (ATDN) vSLAM system can be created. The Embedding Distance Loss function is introduced and using it the ATDN architecture is trained. The resulting system managed to achieve 4.4% translation and 0.0176 deg/m rotational error on a subset of the KITTI dataset. The proposed architecture can be used for efficient and low-latency autonomous driving (AD) aiding database creation as well as a basis for autonomous vehicle (AV) control.Comment: Published in Periodica Polytechnica Electrical Engineering 11 page

A Multi-Sensor Fusion-Based Underwater Slam System

This dissertation addresses the problem of real-time Simultaneous Localization and Mapping (SLAM) in challenging environments. SLAM is one of the key enabling technologies for autonomous robots to navigate in unknown environments by processing information on their on-board computational units. In particular, we study the exploration of challenging GPS-denied underwater environments to enable a wide range of robotic applications, including historical studies, health monitoring of coral reefs, underwater infrastructure inspection e.g., bridges, hydroelectric dams, water supply systems, and oil rigs. Mapping underwater structures is important in several fields, such as marine archaeology, Search and Rescue (SaR), resource management, hydrogeology, and speleology. However, due to the highly unstructured nature of such environments, navigation by human divers could be extremely dangerous, tedious, and labor intensive. Hence, employing an underwater robot is an excellent fit to build the map of the environment while simultaneously localizing itself in the map. The main contribution of this dissertation is the design and development of a real-time robust SLAM algorithm for small and large scale underwater environments. SVIn – a novel tightly-coupled keyframe-based non-linear optimization framework fusing Sonar, Visual, Inertial and water depth information with robust initialization, loop-closing, and relocalization capabilities has been presented. Introducing acoustic range information to aid the visual data, shows improved reconstruction and localization. The availability of depth information from water pressure enables a robust initialization and refines the scale factor, as well as assists to reduce the drift for the tightly-coupled integration. The complementary characteristics of these sensing v modalities provide accurate and robust localization in unstructured environments with low visibility and low visual features – as such make them the ideal choice for underwater navigation. The proposed system has been successfully tested and validated in both benchmark datasets and numerous real world scenarios. It has also been used for planning for underwater robot in the presence of obstacles. Experimental results on datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle (AUV) Aqua2 in challenging underwater environments with poor visibility, demonstrate performance never achieved before in terms of accuracy and robustness. To aid the sparse reconstruction, a contour-based reconstruction approach utilizing the well defined edges between the well lit area and darkness has been developed. In particular, low lighting conditions, or even complete absence of natural light inside caves, results in strong lighting variations, e.g., the cone of the artificial video light intersecting underwater structures and the shadow contours. The proposed method utilizes these contours to provide additional features, resulting into a denser 3D point cloud than the usual point clouds from a visual odometry system. Experimental results in an underwater cave demonstrate the performance of our system. This enables more robust navigation of autonomous underwater vehicles using the denser 3D point cloud to detect obstacles and achieve higher resolution reconstructions