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Journal ArticleDOI

CoSLAM: Collaborative Visual SLAM in Dynamic Environments

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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.

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Citations
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Journal ArticleDOI

Energy-Efficient Ground Traversability Mapping Based on UAV-UGV Collaborative System

TL;DR: Wang et al. as mentioned in this paper proposed a collaborative map fusion algorithm based on Multi-task Gaussian Process Classification (MTGPC) using heterogeneous robotic systems, which can fuse sensor data of different types.
Journal ArticleDOI

Streaming Solutions for Time-Varying Optimization Problems

TL;DR: In this paper , the authors studied streaming optimization problems with objectives of the form $ \sum _{t=1}^{T}f(x_{t-1},x_{ t})$ and showed theoretical guarantees for algorithms with limited memory, showing that limiting the solution updates to a small number of frames in the past sacrifices almost nothing in accuracy.
Posted Content

Better Together: Online Probabilistic Clique Change Detection in 3D Landmark-Based Maps

TL;DR: This work addresses changes in complex three-dimensional environments with the creation of a probabilistic filter that operates on the features that give rise to landmarks, allowing for dynamic and semi-static objects to be removed from a formally static map.
Dissertation

Dynamic Object Tracking and 3-D Visualization from Big Visual Data

Kuan-Hui Lee
TL;DR: This work effectively integrates Visual Simultaneous Localization And Mapping, pedestrian detection, ground plane estimation, and kernel-based tracking techniques and proposes a novel tracking framework, combining the CMK tracking and the estimated 3-D information, to globally optimize the data association between consecutive frames.
References
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Proceedings ArticleDOI

Good features to track

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

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.
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