CORB2I-SLAM: An Adaptive Collaborative Visual-Inertial SLAM for Multiple Robots

Saha, Arindam; Dhara, Bibhas Chandra; Umer, Saiyed; AlZubi, Ahmad Ali; Alanazi, Jazem Mutared; Yurii, Kulakov

doi:10.3390/electronics11182814

Cited by 8 publications

(3 citation statements)

References 34 publications

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“…This principle determines the camera's motion in three-dimensional space by analyzing feature points or descriptors in adjacent image frames, such as corners, edges, or ORB features. By tracking the positional changes of these feature points across different image frames, VO computes the camera's pose changes, encompassing translation and rotation [28].…”

Section: Visual Odometrymentioning

confidence: 99%

Cognitive Enhancement of Robot Path Planning and Environmental Perception Based on Gmapping Algorithm Optimization

Liu,

Gong,

et al. 2024

Electronics

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In the logistics warehouse environment, the autonomous navigation and environment perception of the logistics sorting robot are two key challenges. To deal with the complex obstacles and cargo layout in a warehouse, this study focuses on improving the robot perception and navigation system to achieve efficient path planning and safe motion control. For this purpose, a scheme based on an improved Gmapping algorithm is proposed to construct a high-precision map inside a warehouse through the efficient scanning and processing of environmental data by robots. While the improved algorithm effectively integrates sensor data with robot position information to realize the real-time modeling and analysis of warehouse environments. Consequently, the precise mapping results provide a reliable navigation basis for the robot, enabling it to make intelligent path planning and obstacle avoidance decisions in unknown or dynamic environments. The experimental results show that the robot using the improved Gmapping algorithm has high accuracy and robustness in identifying obstacles and an effectively reduced navigation error, thus improving the intelligence level and efficiency of logistics operations. The improved algorithm significantly enhances obstacle detection rates, increasing them by 4.05%. Simultaneously, it successfully reduces map size accuracy errors by 1.4% and angle accuracy errors by 0.5%. Additionally, the accuracy of the robot’s travel distance improves by 2.4%, and the mapping time is reduced by nine seconds. Significant progress has been made in achieving high-precision environmental perception and intelligent navigation, providing reliable technical support and solutions for autonomous operations in logistics warehouses.

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Section: Visual Odometrymentioning

confidence: 99%

Cognitive Enhancement of Robot Path Planning and Environmental Perception Based on Gmapping Algorithm Optimization

Liu,

Gong,

et al. 2024

Electronics

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“…There are examples of multiagent SLAM [4,18,19] that look toward solving the multiagent problem. Although these examples are proven to work, the identified gap in this paper is in decentralised communication between agents; furthermore, multiagent SLAM remains to be generalised between heterogeneous agents.…”

Section: Slammentioning

confidence: 99%

Information Rich Voxel Grid for Use in Heterogeneous Multi-Agent Robotics

et al. 2023

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Robotic agents are now ubiquitous in both home and work environments; moreover, the degree of task complexity they can undertake is also increasing exponentially. Now that advanced robotic agents are commonplace, the question for utilisation becomes how to enable collaboration of these agents, and indeed, many have considered this over the last decade. If we can leverage the heterogeneous capabilities of multiple agents, not only can we achieve more complex tasks, but we can better position the agents in more chaotic environments and compensate for lacking systems in less sophisticated agents. Environments such as search and rescue, agriculture, autonomous vehicles, and robotic maintenance are just a few examples of complex domains that can leverage collaborative robotics. If the use of a robotic agent is fruitful, the question should be: How can we provide a world state and environment mapping, combined with a communication method, that will allow these robotic agents to freely communicate? Moreover, how can this be decentralised such that agents can be introduced to new and existing environments already understood by other agents? The key problem that is faced is the communication method; however, when looking deeper we also need to consider how the change of an environment is mapped while considering that there are multiple differing sensors. To this end, we present the voxel grid approach for use in a decentralised robotic colony. To validate this, results are presented to show how the single-agent and multiagent systems compare.

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“…Visual collaborative SLAM algorithms include [11] and [12]. CORB2I-SLAM [13] and COVINS [6] both propose centralized visual-inertial collaborative SLAM algorithms built on either ORB-SLAM2 [14] in the case of CORB2I-SLAM or ORB-SLAM3 [15] in the case of COVINS. Both of the methods perform map building and localization with passive sensors, however, the resulting maps lack easy interpretability for humans.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Visual-Inertial Localization of Teams With Floorplan Extraction

Gazdag,

Pasztornicky,

Jankó

et al. 2023

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)

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This paper showcases a real-world example of a system that achieves collaborative localization and mapping of multiple agents within a building. The proposed system processes the odometry and 3D point cloud data collected by the agents moving around the building to automatically generate the building's floorplan on which the agent trajectories are overlaid. The wearable hardware consists of a low-cost passive integrated sensor that includes both a camera and an IMU (Inertial Measurement Unit) and an embedded compute unit. The system's capabilities are shown through real-world experiments.

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CORB2I-SLAM: An Adaptive Collaborative Visual-Inertial SLAM for Multiple Robots

Cited by 8 publications

References 34 publications

Cognitive Enhancement of Robot Path Planning and Environmental Perception Based on Gmapping Algorithm Optimization

Cognitive Enhancement of Robot Path Planning and Environmental Perception Based on Gmapping Algorithm Optimization

Information Rich Voxel Grid for Use in Heterogeneous Multi-Agent Robotics

Collaborative Visual-Inertial Localization of Teams With Floorplan Extraction

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