A SLAM research framework for ROS

Huletski, Arthur; Kartashov, Dmitriy

doi:10.1145/3022211.3022223

Cited by 4 publications

(5 citation statements)

References 11 publications

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“…M any different types of representation of maps such as gragh-based map [22] [23],Landmarks with covariance matrix [24] grid-based map [25] [26] are also used to improve the efficiency of both.…”

Section: Related Workmentioning

confidence: 99%

“…Finally, the output is imported to MAS-DQN to get the action of each agent which will be broadcasted (13)(14). T ð tÀ1 ; a tÀ1 ; r t ; t Þ is added to experience reply buffer (15)(16). After obtaining enough sample information, MAS-DQN will be trained (17)(18)(19)(20)(21).…”

Section: Sequences Of the Proposed Mechanismmentioning

confidence: 99%

“…This step mainly involves multi-agent task allocation (MRTA). In the second step, the selected agent uses its own pose and different types of map such as graph-based map, 11,12 landmarks with covariance matrix, 13 and grid-based map 14,15 to measure or observe the pose of the agent to be localized. Next, we will introduce the related work of these two points separately.…”

Section: Related Workmentioning

confidence: 99%

“…(24)(25)) If x is not assigned a target,it will stop to wait for the assistance. (26)(27)) If x get one target,it will follow the calculated path to approach his intended location and update his assistance status. ) Lif e is a constant,which is set up to prevent assistance from going on indefinitely.…”

mentioning

confidence: 99%

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Active collaboration in relative observation for multi-agent visual simultaneous localization and mapping based on Deep Q Network

Pei

Piao

Quan

et al. 2020

International Journal of Advanced Robotic Systems

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This paper proposes a unique active relative localization mechanism for multi-agent Simultaneous Localization and Mapping(SLAM),in which a agent to be observed are considered as a task, which is performed by others assisting that agent by relative observation. A task allocation algorithm based on deep reinforcement learning are proposed for this mechanism. Each agent can choose whether to localize other agents or to continue independent SLAM on it own initiative. By this way, the process of each agent SLAM will be interacted by the collaboration. Firstly, based on the characteristics of ORBSLAM, a unique observation function which models the whole MAS is obtained. Secondly, a novel type of Deep Q network(DQN) called MAS-DQN is deployed to learn correspondence between Q Value and state-action pair, abstract representation of agents in MAS are learned in the process of collaboration among

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Section: Related Workmentioning

confidence: 99%

Section: Sequences Of the Proposed Mechanismmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Active collaboration in relative observation for multi-agent visual simultaneous localization and mapping based on Deep Q Network

Pei

Piao

Quan

et al. 2020

International Journal of Advanced Robotic Systems

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show abstract

“…With regard to ROS courses, it seems that the lab-driven pedagogy is also a well-considered option with some variations. Huletski and Kartashov [9] employed a prediction-correction method to estimate current robot pose and map that resolve the SLAM (simultaneous localization and mapping) issue on ROS platform. Yousuf, et al [10] adopted project-based pedagogy to teach Electrical Engineering Technology students ROS and kinematics of robotics.…”

Section: The Multiple-lab-driven Pedagogy With Tck Integratedmentioning

confidence: 99%