首次将增量优化用于动态SLAM，实现在线高效建图。
Online Dynamic SLAM with Incremental Smoothing and Mapping
- 采用新型因子图结构与增量优化结合，支持实时更新。
- 在多个数据集上精度达或超过当前最优，速度提升5倍。
- 适合需要实时动态场景建模的机器人应用。
动态SLAM方法同时估计静态与动态场景成分，但现有方法虽准确，计算开销大，不适用于在线应用。本文首次将增量优化技术应用于动态SLAM，提出一种新型因子图建模方式与系统架构，充分利用现有增量优化方法，支持在线估计。在多个数据集上，本方法在相机位姿和物体运动估计精度上达到或优于当前最优水平。我们进一步分析了方法的结构特性，验证其可扩展性，并揭示增量求解动态SLAM的挑战。结果表明，该公式具有适合增量求解的良好问题结构，结合系统架构设计，相较现有方法实现5倍速度提升。
原文摘要 · Abstract (English)
Dynamic SLAM methods jointly estimate for the static and dynamic scene components, however existing approaches, while accurate, are computationally expensive and unsuitable for online applications. In this work, we present the first application of incremental optimisation techniques to Dynamic SLAM. We introduce a novel factor-graph formulation and system architecture designed to take advantage of existing incremental optimisation methods and support online estimation. On multiple datasets, we demonstrate that our method achieves equal to or better than state-of-the-art in camera pose and object motion accuracy. We further analyse the structural properties of our approach to demonstrate its scalability and provide insight regarding the challenges of solving Dynamic SLAM incrementally. Finally, we show that our formulation results in problem structure well-suited to incremental solvers, while our system architecture further enhances performance, achieving a 5x speed-up over existing methods.
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