  Indoor localization systems often fuse inertial odometry with map information
via hand-defined methods to reduce odometry drift, but such methods are
sensitive to noise and struggle to generalize across odometry sources. To
address the robustness problem in map utilization, we propose a data-driven
prior on possible user locations in a map by combining learned spatial map
embeddings and temporal odometry embeddings. Our prior learns to encode which
map regions are feasible locations for a user more accurately than previous
hand-defined methods. This prior leads to a 49% improvement in inertial-only
localization accuracy when used in a particle filter. This result is
significant, as it shows that our relative positioning method can match the
performance of absolute positioning using bluetooth beacons. To show the
generalizability of our method, we also show similar improvements using wheel
encoder odometry.
