Journal ArticleDOI
CoSLAM: Collaborative Visual SLAM in Dynamic Environments
Danping Zou,Ping Tan +1 more
Reads0
Chats0
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
More filters
Journal ArticleDOI
CCM-SLAM: Robust and efficient centralized collaborative monocular simultaneous localization and mapping for robotic teams
Patrik Schmuck,Margarita Chli +1 more
TL;DR: CCM‐SLAM is presented, a centralized collaborative SLAM framework for robotic agents, each equipped with a monocular camera, a communication unit, and a small processing board, that ensures their autonomy as individuals while a central server with potentially bigger computational capacity enables their collaboration.
Journal ArticleDOI
Motion removal for reliable RGB-D SLAM in dynamic environments
TL;DR: This paper proposes a novel RGB-D data-based motion removal approach that is on-line and does not require prior-known moving-object information, such as semantics or visual appearances, and integrates the approach into the front end of anRGB-D SLAM system.
Proceedings ArticleDOI
Multi-UAV collaborative monocular SLAM
TL;DR: A novel, centralized architecture for collaborative monocular SLAM employing multiple small Unmanned Aerial Vehicles (UAVs) to act as agents and allowing an agent to incorporate observations from others in its SLAM estimates on the fly is proposed.
Journal ArticleDOI
Cloud-Based Collaborative 3D Mapping in Real-Time With Low-Cost Robots
TL;DR: This paper presents an architecture, protocol, and parallel algorithms for collaborative 3D mapping in the cloud with low-cost robots, as well as quantitative evaluation of localization accuracy, bandwidth usage, processing speeds, and map storage.
Journal ArticleDOI
On-Road Pedestrian Tracking Across Multiple Driving Recorders
Kuan-Hui Lee,Jenq-Neng Hwang +1 more
TL;DR: A new framework to track on-road pedestrians across multiple driving recorders built upon the results of tracking under a single driving recorder to determine whether a specific pedestrian belongs to one or several cameras' field of views by considering association likelihood of the tracked pedestrians.
References
More filters
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.