资源说明: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
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