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This paper develops a real-time decentralized metric-semantic Simultaneous Localization and Mapping (SLAM) approach that leverages a sparse and lightweight object-based representation to enable a heterogeneous robot team to autonomously explore 3D environments featuring indoor, urban, and forested areas without relying on GPS. We use a hierarchical metric-semantic representation of the environment, including high-level sparse semantic maps of object models and low-level voxel maps. We leverage the informativeness and viewpoint invariance of the high-level semantic map to obtain an effective semantics-driven place-recognition algorithm for inter-robot loop closure detection across aerial and ground robots with different sensing modalities. A communication module is designed to track each robot's observations and those of other robots within the communication range. Such observations are then used to construct a merged map.
Our framework enables real-time decentralized operations onboard robots, allowing them to opportunistically leverage communication. We integrate and deploy our proposed framework on three types of aerial and ground robots. Extensive experimental results show an average inter-robot localization error of 0.22 meters in position and -0.16 degrees in orientation, an object mapping F1 score of 0.92, and a communication packet size of merely 2-3 megabytes per kilometer trajectory with 1,000 landmarks.
Algorithm: We develop a real-time decentralized metric-semantic SLAM framework that supports various types of aerial and ground robots, equipped with LiDAR or RGBD sensors. The framework features: (a) a semantics-driven place recognition algorithm that utilizes sparse and lightweight object-level semantic maps for loop closure and map merging, and (b) a decentralized multi-robot collaboration module that facilitates information sharing, even under intermittent communication conditions.
System integration: We integrate and deploy the proposed framework on a heterogeneous team of robots, demonstrating its capacity to enable semantics-in-the-loop autonomous navigation and exploration in various indoor and outdoor environments. The system operates in real-time, utilizing limited onboard computation and memory resources.
Experiments: We conduct extensive real-world experiments and provide thorough empirical results and analysis that highlight the efficiency, accuracy, and robustness of our system.
@article{liu2024slideslam,
title={SlideSLAM: Sparse, Lightweight, Decentralized Metric-Semantic SLAM for Multi-Robot Navigation},
author={Liu, Xu and Lei, Jiuzhou and Prabhu, Ankit and Tao, Yuezhan and Spasojevic, Igor and Chaudhari, Pratik and Atanasov, Nikolay and Kumar, Vijay},
journal={arXiv preprint arXiv:2406.17249},
year={2024}
}