Locking On: Leveraging Dynamic Vehicle-Imposed Motion Constraints to
  Improve Visual Localization
Stephen Hausler Sourav Garg Punarjay Chakravarty Shubham Shrivastava Ankit Vora Michael Milford
Most 6-DoF localization and SLAM systems use static landmarks but ignore
dynamic objects because they cannot be usefully incorporated into a typical
pipeline. Where dynamic objects have been incorporated, typical approaches have
attempted relatively sophisticated identification and localization of these
objects, limiting their robustness or general utility. In this research, we
propose a middle ground, demonstrated in the context of autonomous vehicles,
using dynamic vehicles to provide limited pose constraint information in a
6-DoF frame-by-frame PnP-RANSAC localization pipeline. We refine initial pose
estimates with a motion model and propose a method for calculating the
predicted quality of future pose estimates, triggered based on whether or not
the autonomous vehicle's motion is constrained by the relative frame-to-frame
location of dynamic vehicles in the environment. Our approach detects and
identifies suitable dynamic vehicles to define these pose constraints to modify
a pose filter, resulting in improved recall across a range of localization
tolerances from $0.25m$ to $5m$, compared to a state-of-the-art baseline single
image PnP method and its vanilla pose filtering. Our constraint detection
system is active for approximately $35\%$ of the time on the Ford AV dataset
and localization is particularly improved when the constraint detection is
active.