用GAN实时修复SLAM生成的2D地图噪声，提升建图质量。
GAN-SLAM: Real-Time GAN Aided Floor Plan Creation Through SLAM
- 结合GAN与SLAM，在2D占用网格上实时去噪补全。
- 相比传统方法，地图更清晰，误差更小，适合复杂环境。
- 适用于机器人建图、自动绘图等下游任务，效果显著。
SLAM是现代自主系统的核心，为机器人及其操作者提供环境理解能力。然而，由于机器人运动的动态性，传统SLAM在生成2D占用网格图（Occupancy Grid Maps, OGM）时常出现误差，影响后续任务如平面图生成。为此，本文提出新型'GAN-SLAM'方法，利用生成对抗网络（GAN）在SLAM过程中对占用网格进行清洗与补全，有效降低噪声和误差。我们还将通常用于3D SLAM的高精度位姿估计技术适配至2D形式，使近年3D LiDAR-odometry带来的质量提升可应用于2D建图。实验表明，该方法在真实世界数据上实现了显著更高的地图保真度与质量，噪声极少。我们在大规模复杂环境中验证了其实时性与有效性，并成功支持了如自动平面图绘制等新下游任务。本方法为基于SLAM的地图构建提供了重要进展，也为GAN在OGM误差修正中的应用提供了新思路。
原文摘要 · Abstract (English)
SLAM is a fundamental component of modern autonomous systems, providing robots and their operators with a deeper understanding of their environment. SLAM systems often encounter challenges due to the dynamic nature of robotic motion, leading to inaccuracies in mapping quality, particularly in 2D representations such as Occupancy Grid Maps. These errors can significantly degrade map quality, hindering the effectiveness of specific downstream tasks such as floor plan creation. To address this challenge, we introduce our novel 'GAN-SLAM', a new SLAM approach that leverages Generative Adversarial Networks to clean and complete occupancy grids during the SLAM process, reducing the impact of noise and inaccuracies introduced on the output map. We adapt and integrate accurate pose estimation techniques typically used for 3D SLAM into a 2D form. This enables the quality improvement 3D LiDAR-odometry has seen in recent years to be effective for 2D representations. Our results demonstrate substantial improvements in map fidelity and quality, with minimal noise and errors, affirming the effectiveness of GAN-SLAM for real-world mapping applications within large-scale complex environments. We validate our approach on real-world data operating in real-time, and on famous examples of 2D maps. The improved quality of the output map enables new downstream tasks, such as floor plan drafting, further enhancing the capabilities of autonomous systems. Our novel approach to SLAM offers a significant step forward in the field, improving the usability for SLAM in mapping-based tasks, and offers insight into the usage of GANs for OGM error correction.
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