  Probabilistic state-estimation approaches offer a principled foundation for
designing localization systems, because they naturally integrate sequences of
imperfect motion and exteroceptive sensor data. Recently, probabilistic
localization systems utilizing appearance-invariant visual place recognition
(VPR) methods as the primary exteroceptive sensor have demonstrated
state-of-the-art performance in the presence of substantial appearance change.
However, existing systems 1) do not fully utilize odometry data within the
motion models, and 2) are unable to handle route deviations, due to the
assumption that query traverses exactly repeat the mapping traverse. To address
these shortcomings, we present a new probabilistic topometric localization
system which incorporates full 3-dof odometry into the motion model and
furthermore, adds an "off-map" state within the state-estimation framework,
allowing query traverses which feature significant route detours from the
reference map to be successfully localized. We perform extensive evaluation on
multiple query traverses from the Oxford RobotCar dataset exhibiting both
significant appearance change and deviations from routes previously traversed.
In particular, we evaluate performance on two practically relevant localization
tasks: loop closure detection and global localization. Our approach achieves
major performance improvements over both existing and improved state-of-the-art
systems.
