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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Posted Content
A Hybrid SLAM and Object Recognition System for Pepper Robot.
TL;DR: A hybrid system combining Simultaneous Localisation and Mapping (SLAM) algorithm with object recognition is developed and tested with Pepper robot in real-world conditions for the first time.
Dissertation
Stratégie d'exploration multirobot fondée sur le calcul de champs de potentiels
TL;DR: In this article, the authors propose a strategy de cooperation for a cartographie multirobot efficace based on the Cart-O-Matic (CArtographie par ROboT d'un TErritoire) organized by l'ANR and la DGA.
Book ChapterDOI
Monocular SLAM System in Dynamic Scenes Based on Semantic Segmentation
TL;DR: This paper proposes to combine the image semantic segmentation based on deep learning method with the traditional visual SLAM framework to reduce the interference of dynamic objects on the positioning results.
Proceedings ArticleDOI
Common field-of-view of cameras in robotic swarms
TL;DR: An adaptive common FOV detection method based on fuzzy plane clustering is proposed and shown to be invariant under baseline scaling and an autonomous grouping algorithm is further proposed with respect to both distance of robots and overlapping FOV of cameras.
Proceedings ArticleDOI
Multi-Camera Matching of Spatio-Temporal Binary Features
TL;DR: This work tracks local binary features, which encode intensity comparisons of pixel pairs in an image patch into fixed-length binary descriptors, and complements the descriptor with a binary vector that identifies intensity comparisons that are temporally unstable.
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.