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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Journal ArticleDOI
Enhancing collaborative road scene reconstruction with unsupervised domain alignment
TL;DR: This work proposes to use multimodal domain adaptation as a preprocessing step to align images in their appearance and enhance keypoint matching across viewing conditions while preserving the advantages of handcrafted features for collaborative reconstruction of road sections by a car fleet.
Journal ArticleDOI
Hybrid Visual SLAM for Underwater Vehicle Manipulator Systems
TL;DR: In this article , a novel visual feature based scene mapping method for underwater vehicle manipulator systems (UVMSs) is presented, with specific emphasis on robust mapping in natural seafloor environments.
Journal ArticleDOI
SimVODIS: Simultaneous Visual Odometry, Object Detection, and Instance Segmentation
TL;DR: SimVODIS as discussed by the authors proposes a neural architecture that simultaneously performs both geometric and semantic tasks in a single thread: simultaneous visual odometry, object detection, and instance segmentation.
Proceedings ArticleDOI
Features detection and matching for visual simultaneous localization and mapping (VSLAM)
TL;DR: The selected feature detection method has been validated and proven efficient for the VSLAM and the local images of matching effect on the detector and descriptor have proved the correctness of key point matching.
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.