  Vision-aided localization for low-cost mobile robots in diverse environments
has attracted widespread attention recently. Although many current systems are
applicable in daytime environments, nocturnal visual localization is still an
open problem owing to the lack of stable visual information. An insight from
most nocturnal scenes is that the static and bright streetlights are reliable
visual information for localization. Hence we propose a nocturnal vision-aided
localization system in streetlight maps with a novel data association and
matching scheme using object detection methods. We leverage the Invariant
Extended Kalman Filter (InEKF) to fuse IMU, odometer, and camera measurements
for consistent state estimation at night. Furthermore, a tracking recovery
module is also designed for tracking failures. Experimental results indicate
that our proposed system achieves accurate and robust localization with less
than $0.2\%$ relative error of trajectory length in four nocturnal
environments.
