Extended Spectra-Based Grid Map Merging With Unilateral Observations for Multi-Robot SLAM

Lee, Heoncheol; Lee, Seung Hwan

doi:10.1109/access.2021.3083936

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…One possibility is to scale up by using multiple robots in conjunction with multiple edge computing instances, as proposed in edge cloud computing. In particular, it will be interesting to integrate the dynamic offloading into collaborative multirobot environments [85]- [88] in future research.…”

Section: Discussionmentioning

confidence: 99%

DynNetSLAM: Dynamic Visual SLAM Network Offloading

et al. 2022

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Existing Visual Simultaneous Localization And Mapping (vSLAM) approaches that offload the complex self-localization computations from mobile robots over a wireless network to edge computing are limited to static offloading, i.e., the offloaded computation tasks are offloaded permanently. However, wireless networks are inherently dynamic and may excessively delay the transmissions between a mobile device and the edge during periods of poor wireless network quality, e.g., from fading or temporary obstructions. We propose and evaluate Dynamic Visual SLAM Network Offloading (DynNetSLAM) to dynamically adapt the vSLAM computation offloading according to the measured wireless network latency. As groundwork towards developing DynNetSLAM, we first enhance the existing state-of-the-art vSLAM approaches through judicious parameter settings and parallel map updates to enable the tracking of common fast vSLAM data sets. We introduce an offloading latency threshold along with a safe zone and a hysteresis around the threshold to control the dynamic offloading. Our extensive evaluations with public vSLAM data sets indicate that DynNetSLAM with the hysteresis substantially reduces the probability of track loss events compared to the state-of-the-art ORB-SLAM2 approach for processing statically on the mobile device and the enhanced static Edge SLAM. Also, DynNetSLAM nearly attains the low absolute position error and only slightly increases the CPU utilization compared to the enhanced static Edge SLAM.

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Section: Discussionmentioning

confidence: 99%

DynNetSLAM: Dynamic Visual SLAM Network Offloading

et al. 2022

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“…The improved map fusion algorithm of Blanco [5] et al mainly obtains their transformation matrix by the corresponding feature points in the two sub-maps constructed, and the remaining ones are divided into feature maps and raster maps, and then the transformation matrix is obtained by the RANSAC algorithm, which has the disadvantage that the resulting transformation matrix is inaccurate. Saeedi [6] et al improved a raster map fusion algorithm, introduced the Hof transform for the constructed raster map, compared the peak of the Hof map to obtain the transformation matrix of the two maps, hypothetically optimized the transformation matrix, optimized the transformation matrix and the Hof map to obtain the translation vector, and optimized the translation vector using entropy adjustment, and finally fused the raster map. The downside of this approach is that the real-time nature of map fusion decreases as map accuracy decreases.…”

Section: Introductionmentioning

confidence: 99%

Real-time fusion study of raster maps based on feature matching

Tong,

Chen,

et al. 2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

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Aiming at the problem of low mapping efficiency of Simultaneous Localization And Mapping (SLAM) algorithm for single robot, a real-time fusion scheme for multi-robot raster maps based on improved map_meiging package is designed, and the PROSAC algorithm with improved RANSAC algorithm is used to eliminate mismatched feature points and improve the efficiency of map fusion. Firstly, the two single robots construct local maps based on the Gmapping algorithm, and then extract the feature points of the raster map after grayscale processing, match and complete the map fusion after purification, which does not need to predict the initial pose of the mobile robot in advance. Finally, test experiments are carried out in the Gazebo simulation environment to verify the effectiveness, real-time and robustness of the method.

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“…Various studies on algorithms are being conducted on simultaneous localization and mapping (SLAM) [ 15 , 16 , 17 ], collision avoidance [ 18 , 19 , 20 ], and formation [ 21 , 22 ] for multi-agent systems. Jang et al [ 23 ] introduced a collaborative monocular SLAM using the rendezvous generated as multiple robots execute tasks.…”

Section: Introductionmentioning

confidence: 99%

Plugin Framework-Based Neuro-Symbolic Grounded Task Planning for Multi-Agent System

Moon

2021

Sensors

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As the roles of robots continue to expand in general, there is an increasing demand for research on automated task planning for a multi-agent system that can independently execute tasks in a wide and dynamic environment. This study introduces a plugin framework in which multiple robots can be involved in task planning in a broad range of areas by combining symbolic and connectionist approaches. The symbolic approach for understanding and learning human knowledge is useful for task planning in a wide and static environment. The network-based connectionist approach has the advantage of being able to respond to an ever-changing dynamic environment. A planning domain definition language-based planning algorithm, which is a symbolic approach, and the cooperative–competitive reinforcement learning algorithm, which is a connectionist approach, were utilized in this study. The proposed architecture is verified through a simulation. It is also verified through an experiment using 10 unmanned surface vehicles that the given tasks were successfully executed in a wide and dynamic environment.

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Extended Spectra-Based Grid Map Merging With Unilateral Observations for Multi-Robot SLAM

Cited by 5 publications

References 21 publications

DynNetSLAM: Dynamic Visual SLAM Network Offloading

DynNetSLAM: Dynamic Visual SLAM Network Offloading

Real-time fusion study of raster maps based on feature matching

Plugin Framework-Based Neuro-Symbolic Grounded Task Planning for Multi-Agent System

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