  Accurate and robust pose estimation plays a crucial role in many robotic
systems. Popular algorithms for pose estimation typically rely on high-fidelity
and high-frequency signals from various sensors. Inclusion of these sensors
makes the system less affordable and much more complicated. In this work we
introduce a novel approach for the robotic odometry which only requires a
single camera and, importantly, can produce reliable estimates given even
extremely low-frequency signal of around one frame per second. The approach is
based on matching image features between the consecutive frames of the video
stream using deep feature matching models. The resulting coarse estimate is
then adjusted by a convolutional neural network, which is also responsible for
estimating the scale of the transition, otherwise irretrievable using only the
feature matching information. We evaluate the performance of the approach in
the AISG-SLA Visual Localisation Challenge and find that while being
computationally efficient and easy to implement our method shows competitive
results with only around $3^{\circ}$ of orientation estimation error and $2m$
of translation estimation error taking the third place in the challenge.
