Normal Distributions Transform Occupancy Map fusion: Simultaneous mapping and tracking in large scale dynamic environments

Autonomous vehicles operating in real-world industrial environments have to overcome numerous challenges, chief among which are the creation of consistent 3D world models and the simultaneous tracking of the vehicle pose with respect to the created maps. In this paper we integrate two recently proposed algorithms in an online, near-realtime mapping and tracking system. Using the Normal Distributions Transform (NDT), a sparse Gaussian Mixture Model, for representation of 3D range scan data, we propose a frame-to-model registration and data fusion algorithm - NDT Fusion. The proposed approach uses a submap indexing system to achieve operation in arbitrarily-sized environments. The approach is evaluated on a publicly available city-block sized data set, achieving accuracy and runtime performance significantly better than current state of the art. In addition, the system is evaluated on a data set covering ten hours of operation and a trajectory of 7.2km in a real-world industrial environment, achieving centimeter accuracy at update rates of 5-10 Hz.