  We present a dense-indirect SLAM system using external dense optical flows as
input. We extend the recent probabilistic visual odometry model VOLDOR [Min et
al. CVPR'20], by incorporating the use of geometric priors to 1) robustly
bootstrap estimation from monocular capture, while 2) seamlessly supporting
stereo and/or RGB-D input imagery. Our customized back-end tightly couples our
intermediate geometric estimates with an adaptive priority scheme managing the
connectivity of an incremental pose graph. We leverage recent advances in dense
optical flow methods to achieve accurate and robust camera pose estimates,
while constructing fine-grain globally-consistent dense environmental maps. Our
open source implementation [https://github.com/htkseason/VOLDOR] operates
online at around 15 FPS on a single GTX1080Ti GPU.
