  Visual localization and mapping is the key technology underlying the majority
of mixed reality and robotics systems. Most state-of-the-art approaches rely on
local features to establish correspondences between images. In this paper, we
present three novel scenarios for localization and mapping which require the
continuous update of feature representations and the ability to match across
different feature types. While localization and mapping is a fundamental
computer vision problem, the traditional setup supposes the same local features
are used throughout the evolution of a map. Thus, whenever the underlying
features are changed, the whole process is repeated from scratch. However, this
is typically impossible in practice, because raw images are often not stored
and re-building the maps could lead to loss of the attached digital content. To
overcome the limitations of current approaches, we present the first principled
solution to cross-descriptor localization and mapping. Our data-driven approach
is agnostic to the feature descriptor type, has low computational requirements,
and scales linearly with the number of description algorithms. Extensive
experiments demonstrate the effectiveness of our approach on state-of-the-art
benchmarks for a variety of handcrafted and learned features.
