Multi Robot SLAM

Multi-robot simultaneous localization and mapping (SLAM) aims to create a consistent map of an environment using data from multiple robots, each simultaneously localizing itself within that map. Current research emphasizes efficient algorithms for distributed data processing and loop closure detection, often employing techniques like variational inference, descriptor distillation for compact feature representation, and graph-based optimization to handle the computational and communication challenges inherent in large-scale multi-robot systems. These advancements are crucial for improving the accuracy and scalability of robotic mapping in diverse applications, such as search and rescue, environmental monitoring, and infrastructure inspection. The development of robust and efficient multi-robot SLAM solutions is driving progress in both robotics and distributed systems.