  Visual localization under large changes in scale is an important capability
in many robotic mapping applications, such as localizing at low altitudes in
maps built at high altitudes, or performing loop closure over long distances.
Existing approaches, however, are robust only up to about a 3x difference in
scale between map and query images.
  We propose a novel combination of deep-learning-based object features and
state-of-the-art SIFT point-features that yields improved robustness to scale
change. This technique is training-free and class-agnostic, and in principle
can be deployed in any environment out-of-the-box. We evaluate the proposed
technique on the KITTI Odometry benchmark and on a novel dataset of outdoor
images exhibiting changes in visual scale of $7\times$ and greater, which we
have released to the public. Our technique consistently outperforms
localization using either SIFT features or the proposed object features alone,
achieving both greater accuracy and much lower failure rates under large
changes in scale.
