In this paper, we present a low-bandwidth centralized collaborative direct monocular SLAM (LCCD-SLAM) for multi-robot systems collaborative mapping. Each agent runs the direct method-based visual odometry (VO) independently, giving the algorithm the advantages of semi-dense point cloud reconstruction...
We have tested CCM-SLAM withUbuntu 16.04(ROS Kinetic with OpenCV 3) as well asUbuntu 18.04(ROS Melodic). It is recommended to use a decently powerful computer for the Server Node to ensure good performance for multi-agent SLAM. 3.1 Set up you environment ...
Multiagent collaborative simultaneous localization and mapping (SLAM) is right at the core of enabling collaboration, such that each agent can colocalize in and build a map of the workspace. The key challenges at the heart of this problem, however, lie with robust communication, efficient data ...
SLAMcollaborative SLAMmulti-robot systemslow bandwidthIn this paper, we present a low-bandwidth centralized collaborative direct monocular SLAM (LCCD-SLAM) for multi-robot systems collaborative mapping. Each agent runs the direct method-based visual odometry (VO) independently, giving the algorithm the...
A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots. IEEE Robot. Autom. Lett. 2016, 1, 661–667. [Google Scholar] [CrossRef] Nguyen, T.; Nguyen, N.D.; Nahavandi, S. Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, ...
In this paper, we propose an efficient multi-robot dense SLAM system that utilizes a centralized structure to alleviate the computational and memory burdens on the agents (i.e. mobile robots). To enable real-time dense mapping of the agent, we design a lightweight and accurate dense mapping ...