VDO-SLAM: A Visual Dynamic Object-aware SLAM System
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by
Jun Zhang and Mina Henein and Robert Mahony and Viorela Ila
2020
Abstract
The scene rigidity assumption, also known as the static world assumption, is
common in SLAM algorithms. Most existing algorithms operating in complex
dynamic environments simplify the problem by removing moving objects from
consideration or tracking them separately. Such strong assumptions limit the
deployment of autonomous mobile robotic systems in a wide range of important
real world applications involving highly dynamic and unstructured environments.
This paper presents VDO-SLAM, a robust object-aware dynamic SLAM system that
exploits semantic information to enable motion estimation of rigid objects in
the scene without any prior knowledge of the objects shape or motion models.
The proposed approach integrates dynamic and static structures in the
environment into a unified estimation framework resulting in accurate robot
pose and spatio-temporal map estimation. We provide a way to extract velocity
estimates from object pose change of moving objects in the scene providing an
important functionality for navigation in complex dynamic environments. We
demonstrate the performance of the proposed system on a number of real indoor
and outdoor datasets. Results show consistent and substantial improvements over
state-of-the-art algorithms. An open-source version of the source code is
available.
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