Visual Odometry (VO) is fundamental to autonomous navigation, robotics, and augmented reality,
with unsupervised approaches eliminating the need for expensive ground-truth labels. However,
these methods struggle when dynamic objects violate the static scene assumption, leading to
erroneous pose estimations. We tackle this problem by uncertainty modeling, which is a commonly
used technique that creates robust masks to filter out dynamic objects and
occlusions without
requiring explicit motion segmentation. Traditional uncertainty modeling considers only
single-frame information, overlooking the uncertainties across consecutive frames. Our key
insight is that uncertainty must be propagated and combined across temporal frames to
effectively identify unreliable regions, particularly in dynamic scenes. To address this
challenge, we introduce Combined Projected Uncertainty VO (CoProU-VO),
a novel end-to-end approach that combines target frame uncertainty with projected reference
frame uncertainty using a
principled probabilistic formulation. Built upon vision transformer backbones, our model
simultaneously learns depth, uncertainty estimation, and camera poses.
Consequently, experiments
on the KITTI and nuScenes datasets demonstrate significant improvements over previous
unsupervised monocular end-to-end two-frame-based methods and exhibit strong performance in
challenging highway scenes where other approaches often fail. Additionally, comprehensive
ablation studies validate the effectiveness of cross-frame uncertainty propagation
