To adapt the SLIM system for collaborative mapping across multiple robots, a decentralized architecture could be implemented, allowing each robot to independently collect and process LiDAR data while contributing to a shared global map. This approach would involve several key components to ensure global consistency and scalability.
First, the system could utilize a communication protocol that enables robots to share their local maps and pose estimates with one another. By employing a consensus-based algorithm, the robots can collaboratively merge their individual maps into a unified global map, ensuring that all contributions are considered and integrated effectively. This could involve using techniques such as distributed optimization or consensus-based pose graph optimization (PGO) to align the different maps while minimizing drift and maintaining accuracy.
Second, the SLIM system could implement a robust loop closure detection mechanism that operates across multiple robots. By leveraging shared landmarks and features identified by different robots, the system can enhance the detection of loop closures, which is crucial for maintaining global consistency. This could be achieved through a shared descriptor space or by employing machine learning techniques to recognize common features across different robot perspectives.
Lastly, to ensure scalability, the SLIM system could adopt a hierarchical mapping approach, where local maps are first merged at the robot level before being integrated into a global map. This would reduce the computational burden on individual robots and allow for efficient management of the mapping process as the number of robots and sessions increases. By maintaining a lightweight representation of the map, such as the parameterized lines and planes used in SLIM, the system can effectively handle the increased data volume while preserving mapping accuracy and consistency across the collaborative network.