Visual-UWB Navigation System for Unknown Environments

Navigation applications relying on the Global Navigation Satellite System (GNSS) are limited in indoor environments and GNSS-denied outdoor terrains such as dense urban or forests. In this paper, we present a novel accurate, robust and low-cost GNSS-independent navigation system, which is composed of a monocular camera and Ultra-wideband (UWB) transceivers. Visual techniques have gained excellent results when computing the incremental motion of the sensor, and UWB methods have proved to provide promising localization accuracy due to the high time resolution of the UWB ranging signals. However, the monocular visual techniques with scale ambiguity are not suitable for applications requiring metric results, and UWB methods assume that the positions of the UWB transceiver anchor are pre-calibrated and known, thus precluding their application in unknown and challenging environments. To this end, we advocate leveraging the monocular camera and UWB to create a map of visual features and UWB anchors. We propose a visual-UWB Simultaneous Localization and Mapping (SLAM) algorithm which tightly combines visual and UWB measurements to form a joint non-linear optimization problem on Lie-Manifold. The 6 Degrees of Freedom (DoF) state of the vehicles and the map are estimated by minimizing the UWB ranging errors and landmark reprojection errors. Our navigation system starts with an exploratory task which performs the real-time visual-UWB SLAM to obtain the global map, then the navigation task by reusing this global map. The tasks can be performed by different vehicles in terms of equipped sensors and payload capability in a heterogeneous team. We validate our system on the public datasets, achieving typical centimeter accuracy and 0.1% scale error.Comment: Proceedings of the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2018

Bird\u27s Eye View: Cooperative Exploration by UGV and UAV

This paper proposes a solution to the problem of cooperative exploration using an Unmanned Ground Vehicle (UGV) and an Unmanned Aerial Vehicle (UAV). More specifically, the UGV navigates through the free space, and the UAV provides enhanced situational awareness via its higher vantage point. The motivating application is search and rescue in a damaged building. A camera atop the UGV is used to track a fiducial tag on the underside of the UAV, allowing the UAV to maintain a fixed pose relative to the UGV. Furthermore, the UAV uses its front facing camera to provide a birds-eye-view to the remote operator, allowing for observation beyond obstacles that obscure the UGV’s sensors. The proposed approach has been tested using a TurtleBot 2 equipped with a Hokuyo laser ranger finder and a Parrot Bebop 2. Experimental results demonstrate the feasibility of this approach. This work is based on several open source packages and the generated code will be available online

Vision-based SLAM for the aerial robot ErleCopter

El objetivo principal de este trabajo, es la implementación de distintos tipos de algoritmos SLAM (mapeado y localización simultáneos) de visión monocular en el robot aéreo ErleCopter, empleando la plataforma software ROS (Robotic Operating System). Para ello se han escogido un conjunto de tres algoritmos ampliamente utilizados en el campo de la visión artificial: PTAM, ORB-SLAM y LSD-SLAM. Así se llevará a cabo un estudio del funcionamiento de los mismos en el ErleCopter. Además empleando dichos algoritmos, y fusionando la información extraída por estos con la información de otros sensores presentes en la plataforma robótica, se realizará un EKF (Extended Kalman Filter), de forma que podamos predecir la localización del robot de una manera más exacta en entornos interiores, ante la ausencia de sistemas GPS. Para comprobar el funcionamiento del sistema se empleará la plataforma de simulación robótica Gazebo. Por último se realizarán pruebas con el robot real, de forma que podamos observar y extraer conclusiones del funcionamiento de estos algoritmos sobre el propio ErleCopter.The main objective of this thesis is the implementation of different SLAM (Simultaneous Localization and Mapping) algorithms within the aerial robot ErleCopter, using the software platform ROS (Robotic Operating System). To do so, a bunch of three widely known and used algorithms in the field of the artificial vision have been chosen: PTAM, ORB-SLAM y LSD-SALM. So a study of the performance of such algorithms will be carried out in this way. Besides, working with such algorithms and fusing their information with the one obtained by other sensors existing within the robotic platform, an EKF (Extended Kalman Filter) will be carried out, in order to localize the robot more accurately in indoor environments, given the lack of GPS. To test the performance of the system, the robotic platform Gazebo will be used in this project. Finally tests will be made with the real robot, in order to observe and draw conclusions from the performance of these algorithms within the ErleCopter itself.Máster Universitario en Ingeniería Industrial (M141

The UMA-VI dataset: Visual–inertial odometry in low-textured and dynamic illumination environments

Artículo de dataset. Disponible en http://mapir.uma.es/work/uma-visual-inertial-datasetThis article presents a visual–inertial dataset gathered in indoor and outdoor scenarios with a handheld custom sensor rig, for over 80 min in total. The dataset contains hardware-synchronized data from a commercial stereo camera (Bumblebee®2), a custom stereo rig, and an inertial measurement unit. The most distinctive feature of this dataset is the strong presence of low-textured environments and scenes with dynamic illumination, which are recurrent corner cases of visual odometry and simultaneous localization and mapping (SLAM) methods. The dataset comprises 32 sequences and is provided with ground-truth poses at the beginning and the end of each of the sequences, thus allowing the accumulated drift to be measured in each case. We provide a trial evaluation of five existing state-of-the-art visual and visual–inertial methods on a subset of the dataset. We also make available open-source tools for evaluation purposes, as well as the intrinsic and extrinsic calibration parameters of all sensors in the rig. The dataset is available for download at http://mapir.uma.es/work/uma-visual-inertial-datase

High Definition, Inexpensive, Underwater Mapping

In this paper we present a complete framework for Underwater SLAM utilizing a single inexpensive sensor. Over the recent years, imaging technology of action cameras is producing stunning results even under the challenging conditions of the underwater domain. The GoPro 9 camera provides high definition video in synchronization with an Inertial Measurement Unit (IMU) data stream encoded in a single mp4 file. The visual inertial SLAM framework is augmented to adjust the map after each loop closure. Data collected at an artificial wreck of the coast of South Carolina and in caverns and caves in Florida demonstrate the robustness of the proposed approach in a variety of conditions.Comment: IEEE Internation Conference on Robotics and Automation, 202

RD-VIO: Robust Visual-Inertial Odometry for Mobile Augmented Reality in Dynamic Environments

It is typically challenging for visual or visual-inertial odometry systems to handle the problems of dynamic scenes and pure rotation. In this work, we design a novel visual-inertial odometry (VIO) system called RD-VIO to handle both of these two problems. Firstly, we propose an IMU-PARSAC algorithm which can robustly detect and match keypoints in a two-stage process. In the first state, landmarks are matched with new keypoints using visual and IMU measurements. We collect statistical information from the matching and then guide the intra-keypoint matching in the second stage. Secondly, to handle the problem of pure rotation, we detect the motion type and adapt the deferred-triangulation technique during the data-association process. We make the pure-rotational frames into the special subframes. When solving the visual-inertial bundle adjustment, they provide additional constraints to the pure-rotational motion. We evaluate the proposed VIO system on public datasets. Experiments show the proposed RD-VIO has obvious advantages over other methods in dynamic environments

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