Online Dynamic SLAM With Incremental Smoothing and Mapping

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 a novel factor-graph formulation and system architecture for Dynamic SLAM that inherently supports incremental optimization and online estimation. This represents the first formulation explicitly designed to leverage incremental inference methods in the dynamic setting. 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$\mathbf {5\times }$speed-up over existing methods.