Deep visual odometry and pose reconstruction through single image depth map and triangulation for terrain relative navigation

Relative navigation solutions for planetary landing in close proximity to the surface have exploited geometry-based solutions of monocular visual odometry (VO) due to their robustness and accuracy. However, they encounter challenges in dynamic and low-texture environments, as well as the issue of scale drift, where errors accumulate over time. Recent advancements in research indicate that deep neural networks can autonomously learn scene depths and relative camera positions without relying on ground truth labels. Despite this, their accuracy still falls short compared to traditional methods, primarily due to the absence of geometric information. A hybrid solution has shown promising results, thus, this paper proposes DepthGlue, a VO pipeline that seamlessly integrates multi-view geometry and deep learning, leveraging single-image depth estimation (SIDE) for scale consistency and a CNN feature tracker and matcher network based on the LightGlue architecture.