Online Dynamic SLAM with Incremental Smoothing and Mapping

Abstract

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 optimisation 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 camera pose and object motion accuracy equal to or better than state-of-the-art. 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 leads to problem structure well-suited to incremental solvers, and our system architecture further enhances performance, achieving a 5x speed-up over existing methods. Code is open-sourced.