D-InLoc++: Indoor Localization in Dynamic Environments. (arXiv:2209.10185v1 [cs.CV])

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++. …

arxiv environments localization