VITAMIN-E: VIsual Tracking And MappINg with Extremely Dense Feature Points

Understand

In this paper, we propose a novel indirect monocular SLAM algorithm called "VITAMIN-E," which is highly accurate and robust as a result of tracking extremely dense feature points.

• Typical indirect methods have difficulty in reconstructing dense geometry because of their careful feature point selection for accurate matching.
• Unlike conventional methods, the proposed method processes an enormous number of feature points by tracking the local extrema of curvature informed by dominant flow estimation.
• Because this may lead to high computational cost during bundle adjustment, we propose a novel optimization technique, the "subspace Gauss--Newton method", that significantly improves the computational efficiency of bundle adjustment by partially updating the variables.