Mapping And Localization In Urban Environments Using Cameras
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Mapping And Localization In Urban Environments Using Cameras
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Author : Henning Lategahn
language : en
Publisher: KIT Scientific Publishing
Release Date : 2014
Mapping And Localization In Urban Environments Using Cameras written by Henning Lategahn and has been published by KIT Scientific Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Computers categories.
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.
Mapping And Localization In Urban Environments Using Cameras
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Author : Henning Lategahn
language : en
Publisher:
Release Date : 2020-10-09
Mapping And Localization In Urban Environments Using Cameras written by Henning Lategahn and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-09 with Technology & Engineering categories.
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.
Image Based Localization In Urban Environments
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Author :
language : en
Publisher:
Release Date : 2010
Image Based Localization In Urban Environments written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
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).
Real Time Dense Simultaneous Localization And Mapping Using Monocular Cameras
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Author : William Nicholas Greene
language : en
Publisher:
Release Date : 2016
Real Time Dense Simultaneous Localization And Mapping Using Monocular Cameras written by William Nicholas Greene and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
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.
Switchable Constraints For Robust Simultaneous Localization And Mapping And Satellite Based Localization
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Author : Niko Sünderhauf
language : en
Publisher: Springer Nature
Release Date : 2023-04-07
Switchable Constraints For Robust Simultaneous Localization And Mapping And Satellite Based Localization written by Niko Sünderhauf and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-07 with Technology & Engineering categories.
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.
On Learning Models Of Appearance For Robust Long Term Visual Navigation
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Author : Lee Eric Clement
language : en
Publisher:
Release Date : 2020
On Learning Models Of Appearance For Robust Long Term Visual Navigation written by Lee Eric Clement and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Simultaneous localization and mapping (SLAM) is a class of techniques that allow robots to navigate unknown environments using onboard sensors. With inexpensive commercial cameras as the primary sensor, visual SLAM has become an important and widely used approach to enabling mobile robot autonomy. However, traditional visual SLAM algorithms use only a fraction of the information available from conventional cameras: in addition to the basic geometric cues typically used in visual SLAM, colour images encode information about the camera itself, environmental illumination, surface materials, vehicle motion, and other factors influencing the image formation process. Moreover, visual localization performance degrades quickly in long-term deployments due to environmental appearance changes caused by lighting, weather, or seasonal effects. This is especially problematic when continuous metric localization is required to drive vision-in-the-loop systems such as autonomous route following. This thesis explores several novel approaches to exploiting additional information from cameras to improve the accuracy and reliability of metric visual SLAM algorithms in short- and long-term deployments. First, we develop a technique for reducing drift error in visual odometry (VO) by estimating the position of a known light source such as the sun using indirect illumination cues available from existing image streams. We build and evaluate hand-engineered and learned models for single-image sun detection and achieve significant reductions in drift error over 30~km of driving in urban and planetary analogue environments. Second, we explore deep image-to-image translation as a means of improving metric visual localization under time-varying illumination. Using images captured under different illumination conditions in a common environment, we demonstrate that localization accuracy and reliability can be substantially improved by learning a many-to-one mapping to a user-selected canonical appearance condition. Finally, we develop a self-supervised method for learning a canonical appearance optimized for high-quality localization. By defining a differentiable surrogate loss function related to the performance of a non-differentiable localization pipeline, we train an optimal RGB-to-grayscale mapping for a given environment, sensor, and pipeline. Using synthetic and real-world long-term vision datasets, we demonstrate significant improvements in localization performance compared to standard grayscale images, enabling continuous metric localization over day-night cycles using a single mapping experience.
Practical Insights On Automotive Slam In Urban Environments
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Author : Piotr Skrzypczynski
language : en
Publisher:
Release Date : 2018
Practical Insights On Automotive Slam In Urban Environments written by Piotr Skrzypczynski and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Technology & Engineering categories.
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.
Robust And Scalable Visual Simultaneous Localization And Mapping In Indoor Environments Using Rgbd Cameras
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Author : Jan Frost
language : en
Publisher:
Release Date : 2016
Robust And Scalable Visual Simultaneous Localization And Mapping In Indoor Environments Using Rgbd Cameras written by Jan Frost and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Visual Navigation For Robots In Urban And Indoor Environments
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Author : Yan Lu
language : en
Publisher:
Release Date : 2015
Visual Navigation For Robots In Urban And Indoor Environments written by Yan Lu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
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
Interlacing Self Localization Moving Object Tracking And Mapping For 3d Range Sensors
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Author : Frank Moosmann
language : en
Publisher: KIT Scientific Publishing
Release Date : 2014-05-13
Interlacing Self Localization Moving Object Tracking And Mapping For 3d Range Sensors written by Frank Moosmann and has been published by KIT Scientific Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-13 with Computers categories.
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.