Multi-Robot Collaborative SLAM in Warehouses: A Review

Abstract

With the rapid growth of e-commerce, Automated Guided Vehicles (AGVs) are increasingly deployed in intelligent warehouses, where Simultaneous Localization and Mapping (SLAM) provides the core localization capability. Warehouses differ from domestic or outdoor scenarios in four respects: highly structured regular rack layouts, quasi-static conditions with dynamic goods movement, GPS-denied weak-network communication, and perceptual degradation caused by long corridors and repetitive textures. This paper reviews recent progress in multi-robot collaborative SLAM tailored for warehouse scenarios. It analyzes warehouse-specific environmental characteristics, discusses single-robot SLAM solutions under weak-network constraints, examines anti-drift localization methods for degraded scenes, and compares multi-robot collaborative architectures and map fusion techniques. The review also identifies a gap between academic algorithms and industrial practice, and points to future directions including semantic-geometric fusion, edge computing, and large-model empowered intelligent warehouses.