Download or read book Mapping and Localization in Urban Environments Using Cameras written by Henning Lategahn and published by KIT Scientific Publishing. This book was released on 2014 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this work we present a system to fully automatically create a highly accurate visual feature map from image data acquired from within a moving vehicle. Moreover, a system for high precision self localization is presented. Furthermore, we present a method to automatically learn a visual descriptor. The map relative self localization is centimeter accurate and allows autonomous driving.

Download or read book Mapping and Localization in Urban Environments Using Cameras written by Henning Lategahn and published by . This book was released on 2020-10-09 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this work we present a system to fully automatically create a highly accurate visual feature map from image data aquired from within a moving vehicle. Moreover, a system for high precision self localization is presented. Furthermore, we present a method to automatically learn a visual descriptor. The map relative self localization is centimeter accurate and allows autonomous driving. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.

Download or read book Image based Localization in Urban Environments written by and published by . This book was released on 2010 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report describes an efficient algorithm to accurately determine the position and orientation of a camera in an outdoor urban environment using camera imagery acquired from a single location on the ground. The requirement to operate using imagery from a single location allows a system using our algorithms to generate instant position estimates and ensures that the approach may be applied to both mobile and immobile ground sensors. Localization is accomplished by registering visible ground images to urban terrain models that are easily generated offline from aerial imagery. Provided there are a sufficient number of buildings in view of the sensor, our approach provides accurate position and orientation estimates, with position estimates that are more accurate than those typically produced by a global positioning system (GPS).

Download or read book Real time Dense Simultaneous Localization and Mapping Using Monocular Cameras written by William Nicholas Greene and published by . This book was released on 2016 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cameras are powerful sensors for robotic navigation as they provide high-resolution environment information (color, shape, texture, etc.), while being lightweight, low-power, and inexpensive. Exploiting such sensor data for navigation tasks typically falls into the realm of monocular simultaneous localization and mapping (SLAM), where both the robot's pose and a map of the environment are estimated concurrently from the imagery produced by a single camera mounted on the robot. This thesis presents a monocular SLAM solution capable of reconstructing dense 3D geometry online without the aid of a graphics processing unit (GPU). The key contribution is a multi-resolution depth estimation and spatial smoothing process that exploits the correlation between low-texture image regions and simple planar structure to adaptively scale the complexity of the generated keyframe depthmaps to the quality of the input imagery. High-texture image regions are represented at higher resolutions to capture fine detail, while low-texture regions are represented at coarser resolutions for smooth surfaces. This approach allows for significant computational savings while simultaneously increasing reconstruction density and quality when compared to the state-of-the-art. Preliminary qualitative results are also presented for an adaptive meshing technique that generates dense reconstructions using only the pixels necessary to represent the scene geometry, which further reduces the computational requirements for fully dense reconstructions.

Download or read book Switchable Constraints for Robust Simultaneous Localization and Mapping and Satellite Based Localization written by Niko Sünderhauf and published by Springer Nature. This book was released on 2023-04-07 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: Simultaneous Localization and Mapping (SLAM) has been a long-standing research problem in robotics. It describes the problem of a robot mapping an unknown environment, while simultaneously localizing in it with the help of the incomplete map. This book describes a technique called Switchable Constraints.Switchable Constraints help to increase the robustness of SLAM against data association errors and in particular against false positive loop closure detections. Such false positive loop closure detections can occur when the robot erroneously assumes it re-observed a landmark it has already mapped or when the appearance of the observed surroundings is very similar to the appearance of other places in the map. Ambiguous observations and appearances are very common in human-made environments such as office floors or suburban streets, making robustness against spurious observations a key challenge in SLAM. The book summarizes the foundations of factor graph-based SLAM techniques. It explains the problem of data association errors before introducing the novel idea of Switchable Constraints. We present a mathematical derivation and probabilistic interpretation of Switchable Constraints along with evaluations on different datasets. The book shows that Switchable Constraints are applicable beyond SLAM problems and demonstrates the efficacy of this technique to improve the quality of satellite-based localization in urban environments, where multipath and non-line-of-sight situations are common error sources.

Download or read book Robust and Scalable Visual Simultaneous Localization and Mapping in Indoor Environments Using RGBD Cameras written by Jan Frost and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robotics Research written by Cédric Pradalier and published by Springer Science & Business Media. This book was released on 2011-05-02 with total page 752 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents a collection of papers presented at the 14th International Symposium of Robotic Research (ISRR). ISRR is the biennial meeting of the International Foundation of Robotic Research (IFRR) and its 14th edition took place in Lucerne, Switzerland, from August 31st to September 3rd, 2009. As for the previous symposia, ISRR 2009 followed up on the successful concept of a mixture of invited contributions and open submissions. Half of the 48 presentations were therefore invited contributions from outstanding researchers selected by the IFRR officers, and half were chosen among the 66 submissions after peer review. This selection process resulted in a truly excellent technical program which, we believe, featured some of the very best of robotic research. Out of the 48 presentations, the 42 papers which were finally submitted for publication are organized in 8 sections that encompass the major research orientations in robotics: Navigation, Control & Planning, Human-Robot Interaction, Manipulation and Humanoids, Learning, Mapping, Multi-Robot Systems, and Micro-Robotics. They represent an excellent snapshot of cutting-edge research in robotics and outline future directions.

Download or read book Practical Insights on Automotive SLAM in Urban Environments written by Piotr Skrzypczynski and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This chapter tackles the issues of simultaneous localization and mapping (SLAM) using laser scanners or vision as a viable alternative to the accurate modes of satellite-based localization, which are popular and easy to implement with modern technology but might fail in many urban scenarios. This chapter considers two state-of-the-art localization algorithms, LOAM and ORB-SLAM3 that use the optimization-based formulation of SLAM and utilize laser and vision sensing, respectively. The focus is on the practical aspects of localization and the accuracy of the obtained trajectories. It contributes to a series of experiments conducted using an electric car equipped with a carefully calibrated multisensory setup with a 3D laser scanner, camera, and a smartphone for collecting the exteroceptive measurements. Results of applying the two different SLAM algorithms to the data sequences collected with the vehicle-based multisensory setup clearly demonstrate that not only the expensive laser sensors but also monocular vision, including the commodity smartphone camera, can be used to obtain off-line reasonably accurate vehicle trajectories in an urban environment.

Download or read book Robots Drones UAVs and UGVs for Operation and Maintenance written by Diego Galar and published by CRC Press. This book was released on 2020-05-07 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: Industrial assets (such as railway lines, roads, pipelines) are usually huge, span long distances, and can be divided into clusters or segments that provide different levels of functionality subject to different loads, degradations and environmental conditions, and their efficient management is necessary. The aim of the book is to give comprehensive understanding about the use of autonomous vehicles (context of robotics) for the utilization of inspection and maintenance activities in industrial asset management in different accessibility and hazard levels. The usability of deploying inspection vehicles in an autonomous manner is explained with the emphasis on integrating the total process. Key Features Aims for solutions for maintenance and inspection problems provided by robotics, drones, unmanned air vehicles and unmanned ground vehicles Discusses integration of autonomous vehicles for inspection and maintenance of industrial assets Covers the industrial approach to inspection needs and presents what is needed from the infrastructure end Presents the requirements for robot designers to design an autonomous inspection and maintenance system Includes practical case studies from industries

Download or read book Advances in Computing and Network Communications written by Sabu M. Thampi and published by Springer Nature. This book was released on 2021-04-20 with total page 705 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the 4th International Conference on Computing and Network Communications (CoCoNet'20), October 14–17, 2020, Chennai, India. The papers presented were carefully reviewed and selected from several initial submissions. The papers are organized in topical sections on Signal, Image and Speech Processing, Wireless and Mobile Communication, Internet of Things, Cloud and Edge Computing, Distributed Systems, Machine Intelligence, Data Analytics, Cybersecurity, Artificial Intelligence and Cognitive Computing and Circuits and Systems. The book is directed to the researchers and scientists engaged in various fields of computing and network communication domains.

Download or read book Visual Navigation for Robots in Urban and Indoor Environments written by Yan Lu and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: As a fundamental capability for mobile robots, navigation involves multiple tasks including localization, mapping, motion planning, and obstacle avoidance. In unknown environments, a robot has to construct a map of the environment while simultaneously keeping track of its own location within the map. This is known as simultaneous localization and mapping (SLAM). For urban and indoor environments, SLAM is especially important since GPS signals are often unavailable. Visual SLAM uses cameras as the primary sensor and is a highly attractive but challenging research topic. The major challenge lies in the robustness to lighting variation and uneven feature distribution. Another challenge is to build semantic maps composed of high-level landmarks. To meet these challenges, we investigate feature fusion approaches for visual SLAM. The basic rationale is that since urban and indoor environments contain various feature types such points and lines, in combination these features should improve the robustness, and meanwhile, high-level landmarks can be defined as or derived from these combinations. We design a novel data structure, multilayer feature graph (MFG), to organize five types of features and their inner geometric relationships. Building upon a two view-based MFG prototype, we extend the application of MFG to image sequence-based mapping by using EKF. We model and analyze how errors are generated and propagated through the construction of a two view-based MFG. This enables us to treat each MFG as an observation in the EKF update step. We apply the MFG-EKF method to a building exterior mapping task and demonstrate its efficacy. Two view based MFG requires sufficient baseline to be successfully constructed, which is not always feasible. Therefore, we further devise a multiple view based algorithm to construct MFG as a global map. Our proposed algorithm takes a video stream as input, initializes and iteratively updates MFG based on extracted key frames; it also refines robot localization and MFG landmarks using local bundle adjustment. We show the advantage of our method by comparing it with state-of-the-art methods on multiple indoor and outdoor datasets. To avoid the scale ambiguity in monocular vision, we investigate the application of RGB-D for SLAM.We propose an algorithm by fusing point and line features. We extract 3D points and lines from RGB-D data, analyze their measurement uncertainties, and compute camera motion using maximum likelihood estimation. We validate our method using both uncertainty analysis and physical experiments, where it outperforms the counterparts under both constant and varying lighting conditions. Besides visual SLAM, we also study specular object avoidance, which is a great challenge for range sensors. We propose a vision-based algorithm to detect planar mirrors. We derive geometric constraints for corresponding real-virtual features across images and employ RANSAC to develop a robust detection algorithm. Our algorithm achieves a detection accuracy of 91.0%. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/155525

Download or read book Interlacing Self Localization Moving Object Tracking and Mapping for 3D Range Sensors written by Frank Moosmann and published by KIT Scientific Publishing. This book was released on 2014-05-13 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work presents a solution for autonomous vehicles to detect arbitrary moving traffic participants and to precisely determine the motion of the vehicle. The solution is based on three-dimensional images captured with modern range sensors like e.g. high-resolution laser scanners. As result, objects are tracked and a detailed 3D model is built for each object and for the static environment. The performance is demonstrated in challenging urban environments that contain many different objects.

Download or read book Proceedings of 2nd International Conference on Computer Vision Image Processing written by Bidyut B. Chaudhuri and published by Springer. This book was released on 2018-05-04 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book provides insights into the Second International Conference on Computer Vision & Image Processing (CVIP-2017) organized by Department of Computer Science and Engineering of Indian Institute of Technology Roorkee. The book presents technological progress and research outcomes in the area of image processing and computer vision. The topics covered in this book are image/video processing and analysis; image/video formation and display; image/video filtering, restoration, enhancement and super-resolution; image/video coding and transmission; image/video storage, retrieval and authentication; image/video quality; transform-based and multi-resolution image/video analysis; biological and perceptual models for image/video processing; machine learning in image/video analysis; probability and uncertainty handling for image/video processing; motion and tracking; segmentation and recognition; shape, structure and stereo.

Download or read book Vision based Localization and Attitude Estimation Methods in Natural Environments written by Bertil Grelsson and published by Linköping University Electronic Press. This book was released on 2019-04-30 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last decade, the usage of unmanned systems such as Unmanned Aerial Vehicles (UAVs), Unmanned Surface Vessels (USVs) and Unmanned Ground Vehicles (UGVs) has increased drastically, and there is still a rapid growth. Today, unmanned systems are being deployed in many daily operations, e.g. for deliveries in remote areas, to increase efficiency of agriculture, and for environmental monitoring at sea. For safety reasons, unmanned systems are often the preferred choice for surveillance missions in hazardous environments, e.g. for detection of nuclear radiation, and in disaster areas after earthquakes, hurricanes, or during forest fires. For safe navigation of the unmanned systems during their missions, continuous and accurate global localization and attitude estimation is mandatory. Over the years, many vision-based methods for position estimation have been developed, primarily for urban areas. In contrast, this thesis is mainly focused on vision-based methods for accurate position and attitude estimates in natural environments, i.e. beyond the urban areas. Vision-based methods possess several characteristics that make them appealing as global position and attitude sensors. First, vision sensors can be realized and tailored for most unmanned vehicle applications. Second, geo-referenced terrain models can be generated worldwide from satellite imagery and can be stored onboard the vehicles. In natural environments, where the availability of geo-referenced images in general is low, registration of image information with terrain models is the natural choice for position and attitude estimation. This is the problem area that I addressed in the contributions of this thesis. The first contribution is a method for full 6DoF (degrees of freedom) pose estimation from aerial images. A dense local height map is computed using structure from motion. The global pose is inferred from the 3D similarity transform between the local height map and a digital elevation model. Aligning height information is assumed to be more robust to season variations than feature-based matching. The second contribution is a method for accurate attitude (pitch and roll angle) estimation via horizon detection. It is one of only a few methods that use an omnidirectional (fisheye) camera for horizon detection in aerial images. The method is based on edge detection and a probabilistic Hough voting scheme. The method allows prior knowledge of the attitude angles to be exploited to make the initial attitude estimates more robust. The estimates are then refined through registration with the geometrically expected horizon line from a digital elevation model. To the best of our knowledge, it is the first method where the ray refraction in the atmosphere is taken into account, which enables the highly accurate attitude estimates. The third contribution is a method for position estimation based on horizon detection in an omnidirectional panoramic image around a surface vessel. Two convolutional neural networks (CNNs) are designed and trained to estimate the camera orientation and to segment the horizon line in the image. The MOSSE correlation filter, normally used in visual object tracking, is adapted to horizon line registration with geometric data from a digital elevation model. Comprehensive field trials conducted in the archipelago demonstrate the GPS-level accuracy of the method, and that the method can be trained on images from one region and then applied to images from a previously unvisited test area. The CNNs in the third contribution apply the typical scheme of convolutions, activations, and pooling. The fourth contribution focuses on the activations and suggests a new formulation to tune and optimize a piecewise linear activation function during training of CNNs. Improved classification results from experiments when tuning the activation function led to the introduction of a new activation function, the Shifted Exponential Linear Unit (ShELU).

Download or read book Experimental Robotics written by Bruno Siciliano and published by Springer Nature. This book was released on 2021-03-27 with total page 630 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the volume of the proceedings for the 17th Edition of ISER. The goal of ISER (International Symposium on Experimental Robotics) symposia is to provide a single-track forum on the current developments and new directions of experimental robotics. The series has traditionally attracted a wide readership of researchers and practitioners interested to the advances and innovations of robotics technology. The 54 contributions cover a wide range of topics in robotics and are organized in 9 chapters: aerial robots, design and prototyping, field robotics, human‒robot interaction, machine learning, mapping and localization, multi-robots, perception, planning and control. Experimental validation of algorithms, concepts, or techniques is the common thread running through this large research collection. Chapter “A New Conversion Method to Evaluate the Hazard Potential of Collaborative Robots in Free Collisions” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Download or read book Monocular Vision Based Localization and Mapping written by Michal Jama and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, two applications related to vision-based localization and mapping are considered: (1) improving navigation system based satellite location estimates by using on-board camera images, and (2) deriving position information from video stream and using it to aid an auto-pilot of an unmanned aerial vehicle (UAV). In the first part of this dissertation, a method for analyzing a minimization process called bundle adjustment (BA) used in stereo imagery based 3D terrain reconstruction to refine estimates of camera poses (positions and orientations) is presented. In particular, imagery obtained with pushbroom cameras is of interest. This work proposes a method to identify cases in which BA does not work as intended, i.e., the cases in which the pose estimates returned by the BA are not more accurate than estimates provided by a satellite navigation systems due to the existence of degrees of freedom (DOF) in BA. Use of inaccurate pose estimates causes warping and scaling effects in the reconstructed terrain and prevents the terrain from being used in scientific analysis. Main contributions of this part of work include: 1) formulation of a method for detecting DOF in the BA; and 2) identifying that two camera geometries commonly used to obtain stereo imagery have DOF. Also, this part presents results demonstrating that avoidance of the DOF can give significant accuracy gains in aerial imagery. The second part of this dissertation proposes a vision based system for UAV navigation. This is a monocular vision based simultaneous localization and mapping (SLAM) system, which measures the position and orientation of the camera and builds a map of the environment using a video-stream from a single camera. This is different from common SLAM solutions that use sensors that measure depth, like LIDAR, stereoscopic cameras or depth cameras. The SLAM solution was built by significantly modifying and extending a recent open-source SLAM solution that is fundamentally different from a traditional approach to solving SLAM problem. The modifications made are those needed to provide the position measurements necessary for the navigation solution on a UAV while simultaneously building the map, all while maintaining control of the UAV. The main contributions of this part include: 1) extension of the map building algorithm to enable it to be used realistically while controlling a UAV and simultaneously building the map; 2) improved performance of the SLAM algorithm for lower camera frame rates; and 3) the first known demonstration of a monocular SLAM algorithm successfully controlling a UAV while simultaneously building the map. This work demonstrates that a fully autonomous UAV that uses monocular vision for navigation is feasible, and can be effective in Global Positioning System denied environments.

Download or read book Monocular Vision Based Particle Filter Localization in Urban Environments written by Keith Yu Kit Leung and published by . This book was released on 2007 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents the design and experimental result of a monocular vision based particle filter localization system for urban settings that uses aerial orthoimagery as a reference map. The topics of perception and localization are reviewed along with their modeling using a probabilistic framework. Computer vision techniques used to create the feature map and to extract features from camera images are discussed. Localization results indicate that the design is viable.