  Autonomous robots often rely on monocular cameras for odometry estimation and
navigation. However, the scale ambiguity problem presents a critical barrier to
effective monocular visual odometry. In this paper, we present CodedVO, a novel
monocular visual odometry method that overcomes the scale ambiguity problem by
employing custom optics to physically encode metric depth information into
imagery. By incorporating this information into our odometry pipeline, we
achieve state-of-the-art performance in monocular visual odometry with a known
scale. We evaluate our method in diverse indoor environments and demonstrate
its robustness and adaptability. We achieve a 0.08m average trajectory error in
odometry evaluation on the ICL-NUIM indoor odometry dataset.
