  Most state-of-the-art localization algorithms rely on robust relative pose
estimation and geometry verification to obtain moving object agnostic camera
poses in complex indoor environments. However, this approach is prone to
mistakes if a scene contains repetitive structures, e.g., desks, tables, boxes,
or moving people. We show that the movable objects incorporate non-negligible
localization error and present a new straightforward method to predict the
six-degree-of-freedom (6DoF) pose more robustly. We equipped the localization
pipeline InLoc with real-time instance segmentation network YOLACT++. The masks
of dynamic objects are employed in the relative pose estimation step and in the
final sorting of camera pose proposal. At first, we filter out the matches
laying on masks of the dynamic objects. Second, we skip the comparison of query
and synthetic images on the area related to the moving object. This procedure
leads to a more robust localization. Lastly, we describe and improve the
mistakes caused by gradient-based comparison between synthetic and query images
and publish a new pipeline for simulation of environments with movable objects
from the Matterport scans. All the codes are available on
github.com/dubenma/D-InLocpp .
