Journal ArticleDOI
CoSLAM: Collaborative Visual SLAM in Dynamic Environments
Danping Zou,Ping Tan +1 more
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TLDR
Experimental results demonstrate that the vision-based simultaneous localization and mapping in dynamic environments with multiple cameras can work robustly in highly dynamic environments and produce more accurate results in static environments.Abstract:
This paper studies the problem of vision-based simultaneous localization and mapping (SLAM) in dynamic environments with multiple cameras. These cameras move independently and can be mounted on different platforms. All cameras work together to build a global map, including 3D positions of static background points and trajectories of moving foreground points. We introduce intercamera pose estimation and intercamera mapping to deal with dynamic objects in the localization and mapping process. To further enhance the system robustness, we maintain the position uncertainty of each map point. To facilitate intercamera operations, we cluster cameras into groups according to their view overlap, and manage the split and merge of camera groups in real time. Experimental results demonstrate that our system can work robustly in highly dynamic environments and produce more accurate results in static environments.read more
Citations
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Book ChapterDOI
MeshFlow: Minimum Latency Online Video Stabilization
TL;DR: The quantitative and qualitative evaluations show that the proposed technique for online video stabilization with only one frame latency using a novel MeshFlow motion model can produce comparable results with the state-of-the-art off-line methods.
Journal ArticleDOI
Online generative model personalization for hand tracking
TL;DR: A new algorithm for real-time hand tracking on commodity depth-sensing devices that learns the geometry as the user performs live in front of the camera, thus enabling seamless virtual interaction at the consumer level is presented.
Book ChapterDOI
HybridFusion: Real-Time Performance Capture Using a Single Depth Sensor and Sparse IMUs
TL;DR: The method combines non-rigid surface tracking and volumetric fusion to simultaneously reconstruct challenging motions, detailed geometries and the inner human body of a clothed subject to enable practical human performance capture that is real-time, robust, low-cost and easy to deploy.
Posted Content
MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects
TL;DR: MaskFusion as mentioned in this paper is a real-time object-aware, semantic and dynamic RGB-D SLAM system that goes beyond traditional systems which output a purely geometric map of a static scene.
Journal ArticleDOI
A Review of Visual-Inertial Simultaneous Localization and Mapping from Filtering-Based and Optimization-Based Perspectives
TL;DR: This study is the first to review Visual-inertial simultaneous localization and mapping techniques from filtering-based and optimization-based perspectives and proposes future development trends and research directions for VI-SLAM.
References
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Proceedings ArticleDOI
Good features to track
Jianbo Shi,Tomasi +1 more
TL;DR: A feature selection criterion that is optimal by construction because it is based on how the tracker works, and a feature monitoring method that can detect occlusions, disocclusions, and features that do not correspond to points in the world are proposed.
Proceedings ArticleDOI
Parallel Tracking and Mapping for Small AR Workspaces
Georg Klein,David W. Murray +1 more
TL;DR: A system specifically designed to track a hand-held camera in a small AR workspace, processed in parallel threads on a dual-core computer, that produces detailed maps with thousands of landmarks which can be tracked at frame-rate with accuracy and robustness rivalling that of state-of-the-art model-based systems.
Journal ArticleDOI
MonoSLAM: Real-Time Single Camera SLAM
TL;DR: The first successful application of the SLAM methodology from mobile robotics to the "pure vision" domain of a single uncontrolled camera, achieving real time but drift-free performance inaccessible to structure from motion approaches is presented.
Journal ArticleDOI
Simultaneous localization and mapping: part I
TL;DR: This paper describes the simultaneous localization and mapping (SLAM) problem and the essential methods for solving the SLAM problem and summarizes key implementations and demonstrations of the method.
Proceedings ArticleDOI
Real-time simultaneous localisation and mapping with a single camera
TL;DR: This work presents a top-down Bayesian framework for single-camera localisation via mapping of a sparse set of natural features using motion modelling and an information-guided active measurement strategy, in particular addressing the difficult issue of real-time feature initialisation via a factored sampling approach.