A hybrid bundle adjustment/pose-graph approach to VSLAM/GPS fusion for low-capacity platforms
Résumé
We focus on the real-time fusion of monocular
visual SLAM with GPS data in order to obtain city-scale,
georeferenced pose estimations and reconstructions. Recently,
GPS/VSLAM fusion through constrained local key-frame based
Bundle Adjustment (BA) using Barrier Term Optimization
(BTO) has proven to be (to the best of our knowledge) the most
robust and accurate method. However, this approach requires a
higher number of cameras to be considered in the optimization:
in practice, more than 30 cameras are necessary, while a
typical vision-only BA can succeed with as few as 10 cameras.
This problem dimensionality makes the method unsuitable for
autonomous embedded platforms of low computational capacity
(e.g. MAVs). In this paper, we present a hybrid constrained
BA/pose-graph approach using BTO, which is motivated by
theoretical observations about covariance changes as a function
of the gauge. We show that our method has desirable properties
that allows its successful use in a BTO context, and present
two different formulations. The experimental validation of
our method shows that both our formulations reduce the
computational cost in comparison with constrained BA using
BTO, without any significant loss of precision. In particular,
our first formulation yields a 60% reduction in execution time.