Simultaneous Localization and Mapping (SLAM) of Mobile Robots

Dear Colleagues,

Simultaneous Localization and Mapping (SLAM) has long been a fundamental and challenging technology in enabling mobile robots to operate autonomously in unknown environments, where safety and precision are the vital issues for reliable and high-level autonomous applications. This Special Issue seeks to gather insights into state-of-the-art research and to promote the continuous progress of SLAM technology for mobile robots. Topics of interest for this Special Issue include, but are not limited to, the following:

1. Advanced SLAM Algorithm Development: The improvement of existing algorithms to increase the accuracy and robustness of localization and mapping.
2. AI and Machine Learning for SLAM: The integration of advanced AI and machine learning techniques with SLAM algorithms in dynamic and complex environments.
3. Expansion of Application Scenarios: Applications of SLAM technology in new scenarios, such as environmental monitoring, autonomous delivery, and cultural heritage exploration.
4. Cooperative SLAM (C-SLAM): Multi-robot SLAM system development, dealing with communication barriers, co-localization issues, and joint optimization problems to enable efficient and effective multi-robot navigation.
5. Fast SLAM for Edge Deployment: Real-time SLAM on edge devices, Hardware–Software co-design optimization, efficient data processing technologies, and resource-constrained SLAM solutions.

Dr. Zhan Wang
Dr. Jin Wang
Guest Editors

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