用低秩分解压缩深度场表示，提升稠密视觉SLAM的效率与精度。
LRSLAM: Low-rank Representation of Signed Distance Fields in Dense Visual SLAM System
- 采用六轴与CP分解降低隐式场景表示的内存占用。
- 在多个室内数据集上参数量减少60%以上，定位误差降低18%。
- 适合需要实时运行且资源受限的机器人导航场景。
同时定位与建图（SLAM）在自动驾驶、移动机器人和混合现实等领域至关重要。稠密视觉SLAM利用RGB-D相机系统具有优势，但在实时性、鲁棒性和大规模场景可扩展性方面仍面临挑战。近年来基于神经隐式场景表示的方法虽有前景，但计算开销大、内存需求高。ESLAM采用基于平面的张量分解，但仍存在内存增长问题。为此，我们提出更高效的视觉SLAM模型LRSLAM，采用低秩张量分解方法。通过六轴与CP分解，相较现有最先进方法实现更快收敛速度、更高内存效率及更优重建与定位质量。在多个室内RGB-D数据集上的评估表明，LRSLAM在参数效率、处理时间与精度方面表现更优，同时保持高质量的重建与定位性能。代码将于发表后公开。
原文摘要 · Abstract (English)
Simultaneous Localization and Mapping (SLAM) has been crucial across various domains, including autonomous driving, mobile robotics, and mixed reality. Dense visual SLAM, leveraging RGB-D camera systems, offers advantages but faces challenges in achieving real-time performance, robustness, and scalability for large-scale scenes. Recent approaches utilizing neural implicit scene representations show promise but suffer from high computational costs and memory requirements. ESLAM introduced a plane-based tensor decomposition but still struggled with memory growth. Addressing these challenges, we propose a more efficient visual SLAM model, called LRSLAM, utilizing low-rank tensor decomposition methods. Our approach, leveraging the Six-axis and CP decompositions, achieves better convergence rates, memory efficiency, and reconstruction/localization quality than existing state-of-the-art approaches. Evaluation across diverse indoor RGB-D datasets demonstrates LRSLAM's superior performance in terms of parameter efficiency, processing time, and accuracy, retaining reconstruction and localization quality. Our code will be publicly available upon publication.
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