  We present a real-time stereo visual-inertial-SLAM system which is able to
recover from complicatedkidnap scenarios and failures online in realtime. We
propose to learn the whole-image-descriptorin a weakly supervised manner based
on NetVLAD and decoupled convolutions. We analyse thetraining difficulties in
using standard loss formulations and propose an allpairloss and show itseffect
through extensive experiments. Compared to standard NetVLAD, our network takes
an orderof magnitude fewer computations and model parameters, as a result runs
about three times faster.We evaluate the representation power of our descriptor
on standard datasets with precision-recall.Unlike previous loop detection
methods which have been evaluated only on fronto-parallel revisits,we evaluate
the performace of our method with competing methods on scenarios involving
largeviewpoint difference. Finally, we present the fully functional system with
relative computation andhandling of multiple world co-ordinate system which is
able to reduce odometry drift, recover fromcomplicated kidnap scenarios and
random odometry failures. We open source our fully functional system as an
add-on for the popular VINS-Fusion.
