MAGiC-SLAM: Multi-Agent Gaussian Globally Consistent SLAM (2411.16785v1)

Abstract: Simultaneous localization and mapping (SLAM) systems with novel view synthesis capabilities are widely used in computer vision, with applications in augmented reality, robotics, and autonomous driving. However, existing approaches are limited to single-agent operation. Recent work has addressed this problem using a distributed neural scene representation. Unfortunately, existing methods are slow, cannot accurately render real-world data, are restricted to two agents, and have limited tracking accuracy. In contrast, we propose a rigidly deformable 3D Gaussian-based scene representation that dramatically speeds up the system. However, improving tracking accuracy and reconstructing a globally consistent map from multiple agents remains challenging due to trajectory drift and discrepancies across agents' observations. Therefore, we propose new tracking and map-merging mechanisms and integrate loop closure in the Gaussian-based SLAM pipeline. We evaluate MAGiC-SLAM on synthetic and real-world datasets and find it more accurate and faster than the state of the art.

• The paper introduces a scalable multi-agent SLAM system using 3D Gaussian-based scene representation to enhance tracking accuracy and real-time performance.
• It integrates an innovative loop closure mechanism and efficient map merging strategy to overcome limitations of previous methods confined to two agents.
• Experimental results on synthetic and real-world datasets demonstrate its superior tracking and rendering performance compared to state-of-the-art SLAM systems.

Overview of MAGiC-SLAM: Multi-Agent Gaussian Globally Consistent SLAM

The paper "MAGiC-SLAM: Multi-Agent Gaussian Globally Consistent SLAM" presents a multi-agent SLAM system that significantly enhances novel view synthesis (NVS) capabilities. MAGiC-SLAM addresses inherent limitations of previous multi-agent NVS-capable SLAM methods, such as limited tracking accuracy, restricted scalability to two agents, and slow processing speeds. The authors introduce a unified framework that incorporates a 3D Gaussian-based scene representation, loop closure integration, and an innovative map merging strategy to provide accurate and efficient multi-agent SLAM.

Key Contributions

The paper's primary contributions are:

1. Multi-Agent SLAM System: MAGiC-SLAM accommodates an arbitrary number of agents in its framework, making it more flexible and scalable than previous methods limited to two agents. This is particularly advantageous for applications requiring cooperative robots or systems in dynamic, large-scale environments.
2. 3D Gaussian-Based Scene Representation: Using 3D Gaussians allows the system to manage rigid body transformations efficiently, facilitating sub-map creation and merging processes. This choice of scene representation also accelerates the SLAM pipeline significantly, making MAGiC-SLAM suitable for real-time applications.
3. Loop Closure Mechanism: The system integrates loop closure within a Gaussian-based SLAM pipeline to enhance trajectory accuracy. This mechanism utilizes a foundational vision model for robust loop detection, improving generalization to unseen environments.
4. Efficiency in Map Merging: The proposed map merging strategy, which emphasizes efficient map optimization and fusion, reduces storage requirements and processing time, addressing a notable challenge in SLAM systems managing large numbers of agents and sub-maps.

Experimental Validation

The authors validate MAGiC-SLAM's performance on synthetic and real-world multi-agent datasets: the ReplicaMultiagent and AriaMultiagent datasets. The system demonstrates superior tracking accuracy, outperforming existing state-of-the-art multi-agent SLAM systems, such as CP-SLAM, and single-agent systems like ORB-SLAM3. The robustness of its tracking is primarily attributed to the novel two-stage tracking approach and the rigorously designed loop closure mechanisms.

In rendering evaluations, MAGiC-SLAM significantly outperforms CP-SLAM in terms of PSNR, SSIM, and LPIPS, effectively rendering both real-world and synthetic data. Its robust performance in rendering tasks is primarily due to the 3D Gaussian scene representation and the cohesion achieved through efficient map merging.

Implications and Future Directions

This work has practical implications for diverse applications such as autonomous driving, robotics, and augmented reality, where accurate environmental mapping and localization are critical. The efficient handling of multiple agents and the system's robust mapping capabilities make it suitable for deployment in large-scale, complex environments.

Future developments could address the slight computational delay per frame, which is mostly attributed to the exhaustive optimization required in implicit tracking mechanisms. Improvements might involve enhancing the convergence speed of tracking without sacrificing accuracy or exploring machine learning-based approaches to reduce computational overhead and potentially improve generalization capabilities.

In conclusion, MAGiC-SLAM presents a substantial advancement in the field of multi-agent SLAM, offering both theoretical and practical innovations. By leveraging modern vision models and optimizing computational strategies, it effectively pushes the frontiers of multi-agent localization and mapping systems.