多智能体3D高斯地图构建，实现快速精准全局一致定位
MAGiC-SLAM: Multi-Agent Gaussian Globally Consistent SLAM
- 基于可刚性变形的3D高斯表示，加速多机协同建图
- 在真实与合成数据上优于现有方法，追踪精度与速度双提升
- 支持多智能体闭环检测，适合机器人集群与自动驾驶场景
同时定位与建图（SLAM）系统在计算机视觉中广泛应用，涉及增强现实、机器人和自动驾驶等领域。然而，现有方法仅限于单智能体运行。近期工作尝试通过分布式神经场景表示解决此问题，但存在速度慢、真实数据渲染不准确、仅支持两智能体、追踪精度有限等问题。为此，本文提出一种刚性可变形的3D高斯场景表示，显著提升系统速度。然而，多智能体轨迹漂移与观测差异仍导致全局一致性建图困难。因此，我们设计新的追踪与地图融合机制，并将闭环检测集成至高斯基SLAM流程。在合成与真实世界数据集上评估表明，MAGiC-SLAM在精度与速度上均超越当前最优方法。
原文摘要 · Abstract (English)
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.
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