  Visual localization is a fundamental task that regresses the 6 Degree Of
Freedom (6DoF) poses with image features in order to serve the high precision
localization requests in many robotics applications. Degenerate conditions like
motion blur, illumination changes and environment variations place great
challenges in this task. Fusion with additional information, such as sequential
information and Inertial Measurement Unit (IMU) inputs, would greatly assist
such problems. In this paper, we present an efficient client-server visual
localization architecture that fuses global and local pose estimations to
realize promising precision and efficiency. We include additional geometry
hints in mapping and global pose regressing modules to improve the measurement
quality. A loosely coupled fusion policy is adopted to leverage the computation
complexity and accuracy. We conduct the evaluations on two typical open-source
benchmarks, 4Seasons and OpenLORIS. Quantitative results prove that our
framework has competitive performance with respect to other state-of-the-art
visual localization solutions.
