  We present AB-VINS, a different kind of visual-inertial SLAM system. Unlike
most VINS systems which only use hand-crafted techniques, AB-VINS makes use of
three different deep networks. Instead of estimating sparse feature positions,
AB-VINS only estimates the scale and bias parameters (a and b) of monocular
depth maps, as well as other terms to correct the depth using multi-view
information which results in a compressed feature state. Despite being an
optimization-based system, the main VIO thread of AB-VINS surpasses the
efficiency of a state-of-the-art filter-based method while also providing dense
depth. While state-of-the-art loop-closing SLAM systems have to relinearize a
number of variables linear the number of keyframes, AB-VINS can perform loop
closures while only affecting a constant number of variables. This is due to a
novel data structure called the memory tree, in which the keyframe poses are
defined relative to each other rather than all in one global frame, allowing
for all but a few states to be fixed. AB-VINS is not as accurate as
state-of-the-art VINS systems, but it is shown through careful experimentation
to be more robust.
