Abstract:
Recent research on mobile robotics has produced new designs that provide
house-hold robots with omnidirectional motion. The image sensor embedded
in these devices motivates the application of 3D vision techniques on them
for navigation and mapping purposes. In addition to this, distributed cheapsensing
systems acting as unitary entity have recently been discovered as an
efficient alternative to expensive mobile equipment.
In this work we present an implementation of a visual reconstruction method,
structure from motion (SfM), on a low-budget, omnidirectional mobile platform,
and extend this method to distributed 3D scene reconstruction with
several instances of such a platform.
Our approach overcomes the challenges yielded by the plaform. The unprecedented
levels of noise produced by the image compression typical of
the platform is processed by our feature filtering methods, which ensure
suitable feature matching populations for epipolar geometry estimation by
means of a strict quality-based feature selection. The robust pose estimation
algorithms implemented, along with a novel feature tracking system,
enable our incremental SfM approach to novelly deal with ill-conditioned
inter-image configurations provoked by the omnidirectional motion. The
feature tracking system developed efficiently manages the feature scarcity
produced by noise and outputs quality feature tracks, which allow robust
3D mapping of a given scene even if - due to noise - their length is shorter
than what it is usually assumed for performing stable 3D reconstructions.
The distributed reconstruction from multiple instances of SfM is attained
by applying loop-closing techniques. Our multiple reconstruction system
merges individual 3D structures and resolves the global scale problem with
minimal overlaps, whereas in the literature 3D mapping is obtained by overlapping
stretches of sequences. The performance of this system is demonstrated
in the 2-session case.
The management of noise, the stability against ill-configurations and the
robustness of our SfM system is validated on a number of experiments and
compared with state-of-the-art approaches. Possible future research areas
are also discussed.
