IWACCE-SAMR' 24

SLAM and Mobile Robots

The core of SLAM (Simultaneous Localization and Mapping) is to enable a robot to simultaneously determine its own position in an unknown environment and construct a map of the environment. Mobile robotics involves robot hardware design, control systems, navigation algorithms, obstacle avoidance strategies, path planning, and multi-robot collaboration. Modern mobile robots can not only work in structured environments (e.g., factories, warehouses), but also perform tasks in complex unstructured environments (e.g., outdoor terrains, underwater, space.) SLAM and mobile robotics are closely related and rapidly developing fields that are driving robotics in the direction of greater intelligence, autonomy, and practicality. As technology continues to advance, we can expect to see more innovative robotic applications in the future, improving people's quality of life and work efficiency.

IWACCE- SAMR’24 Topics of interest include, but are not limited to the following:

• Algorithm optimization: Improve the accuracy, stability and computational efficiency of SLAM algorithm
• Multi-sensor fusion: Combine different types of sensor information to improve the robustness of localization and map construction
• Long-term operation and adaptability: Research on how to make the robot maintain high accuracy in long-time operation and how to adapt to environmental changes
• Large-scale environment and complex scenes: Expand the ability of SLAM technology to be applied in large-scale and complex scenes
• Multi-robot Collaborative SLAM: To realize collaborative localization and mapping between multi-robot systems for larger scale tasks
• Deep learning-based approach: using deep neural networks to improve feature extraction and environment understanding
• Automated Guided Vehicles: such as driverless cars, AGVs (Automated Guided Vehicles)
• Service robots: such as home cleaning robots, hospital service robots
• Exploration robots: such as space exploration, deep sea exploration
• Security monitoring: such as unmanned patrol aircraft, border monitoring
• Agricultural automation: such as automatic harvester, intelligent spraying system

IWACCE- SAMR’24 Session chair

Prof. Qixin Cao, Shanghai Jiao Tong University, China.

Research Interests: Machine Vision, Ubiquitous Robotics, Medical Robot, Mobile Robot