A hybrid bundle adjustment/pose-graph approach to VSLAM/GPS fusion for low-capacity platforms

International audienceWe focus on the real-time fusion of monocularvisual SLAM with GPS data in order to obtain city-scale,georeferenced pose estimations and reconstructions. Recently,GPS/VSLAM fusion through constrained local key-frame basedBundle Adjustment (BA) using Barrier Term Optimization(BTO) has proven to be (to the best of our knowledge) the mostrobust and accurate method. However, this approach requires ahigher number of cameras to be considered in the optimization:in practice, more than 30 cameras are necessary, while atypical vision-only BA can succeed with as few as 10 cameras.This problem dimensionality makes the method unsuitable forautonomous embedded platforms of low computational capacity(e.g. MAVs). In this paper, we present a hybrid constrainedBA/pose-graph approach using BTO, which is motivated bytheoretical observations about covariance changes as a functionof the gauge. We show that our method has desirable propertiesthat allows its successful use in a BTO context, and presenttwo different formulations. The experimental validation ofour method shows that both our formulations reduce thecomputational cost in comparison with constrained BA usingBTO, without any significant loss of precision. In particular,our first formulation yields a 60% reduction in execution time