Loop-box: Multi-Agent Direct SLAM Triggered by Single Loop Closure for Large-Scale Mapping
This addresses the challenge of multi-agent SLAM for large-scale 3D reconstruction, offering a sensor-light approach, though it appears incremental as it builds on existing loop closure and optimization techniques.
The paper tackles the problem of large-scale 3D mapping with multiple agents by developing a framework that uses only monocular cameras to achieve real-time localization and global mapping after the first loop closure between agents, enabling accurate scale difference calculation and pose transformation.
In this paper, we present a multi-agent framework for real-time large-scale 3D reconstruction applications. In SLAM, researchers usually build and update a 3D map after applying non-linear pose graph optimization techniques. Moreover, many multi-agent systems are prevalently using odometry information from additional sensors. These methods generally involve intensive computer vision algorithms and are tightly coupled with various sensors. We develop a generic method for the keychallenging scenarios in multi-agent 3D mapping based on different camera systems. The proposed framework performs actively in terms of localizing each agent after the first loop closure between them. It is shown that the proposed system only uses monocular cameras to yield real-time multi-agent large-scale localization and 3D global mapping. Based on the initial matching, our system can calculate the optimal scale difference between multiple 3D maps and then estimate an accurate relative pose transformation for large-scale global mapping.