# SlideSLAM: Sparse, Lightweight, Decentralized Metric-Semantic SLAM for Multi-Robot Navigation

arXiv: https://arxiv.org/abs/2406.17249 · HTML: https://mlsanity.com/paper/2406.17249/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2024.

## Built on by (most cited first, 11 of 11)

- [SPINE: Online Semantic Planning for Missions with Incomplete Natural Language Specifications in Unstructured Environments](https://mlsanity.com/paper/2410.03035/) (2024)
- [DROID-SLAM in the Wild](https://mlsanity.com/paper/2603.19076/) (2026)
- [SG-Reg: Generalizable and Efficient Scene Graph Registration](https://mlsanity.com/paper/2504.14440/) (2025)
- [Language-Grounded Hierarchical Planning and Execution with Multi-Robot 3D Scene Graphs](https://mlsanity.com/paper/2506.07454/) (2025)
- [Communication- and Computation-Efficient Distributed Submodular Optimization in Robot Mesh Networks](https://mlsanity.com/paper/2407.10382/) (2024)
- [Semantic Exploration and Dense Mapping of Complex Environments using Ground Robot with Panoramic LiDAR-Camera Fusion](https://mlsanity.com/paper/2505.22880/) (2025)
- [Semi-distributed Cross-modal Air-Ground Relative Localization](https://mlsanity.com/paper/2511.06749/) (2025)
- [OpenNavMap: Multi-Session Appearance-Based Topometric Mapping for Scalable Visual Navigation](https://mlsanity.com/paper/2601.12291/) (2026)
- [Hydra++: Real-Time Hierarchical 3D Scene Graph Construction With Object-Level Shape Estimation](https://mlsanity.com/paper/2607.09455/) (2026)
- [SCOUT-SLAM: Structurally-Coupled Dual Uncertainty-Aware 3DGS SLAM in the Wild](https://mlsanity.com/paper/2609.14634/) (2026)
- [CLoSeR: Closing the Loop for Long-Context Streaming Reconstruction](https://mlsanity.com/paper/2610.01927/) (2026)

## Abstract (arXiv)

This paper develops a real-time decentralized metric-semantic SLAM algorithm that enables a heterogeneous robot team to collaboratively construct object-based metric-semantic maps. The proposed framework integrates a data-driven front-end for instance segmentation from either RGBD cameras or LiDARs and a custom back-end for optimizing robot trajectories and object landmarks in the map. To allow multiple robots to merge their information, we design semantics-driven place recognition algorithms that leverage the informativeness and viewpoint invariance of the object-level metric-semantic map for inter-robot loop closure detection. A communication module is designed to track each robot's observations and those of other robots whenever communication links are available. The framework supports real-time, decentralized operation onboard the robots and has been integrated with three types of aerial and ground platforms. We validate its effectiveness through experiments in both indoor and outdoor environments, as well as benchmarks on public datasets and comparisons with existing methods. The framework is open-sourced and suitable for both single-agent and multi-robot real-time metric-semantic SLAM applications. The code is available at: https://github.com/KumarRobotics/SLIDE_SLAM.