Fast 3D mapping in highly dynamic environments using normal distributions transform occupancy maps

Autonomous vehicles operating in real-world industrial environments have to overcome numerous challenges, chief among which is the creation and maintenance of consistent 3D world models. This paper focuses on a particularly important challenge: mapping in dynamic environments. We introduce several improvements to the recently proposed Normal Distributions Transform Occupancy Map (NDT-OM) aimed for efficient mapping in dynamic environments. A careful consistency analysis is given based on convergence and similarity metrics specifically designed for evaluation of NDT maps in dynamic environments. We show that in the context of mapping with known poses the proposed method results in improved consistency and in superior runtime performance, when compared against 3D occupancy grids at the same size and resolution. Additionally, we demonstrate that NDT-OM features real-time performance in a highly dynamic 3D mapping and tracking scenario with centimeter accuracy over a 1.5km trajectory.