  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. We concurrently generate meshes from the reconstructed points and
merge them for an entire 3D model. The experimental results on the SLAM
benchmark dataset EuRoC demonstrated that the proposed method outperformed
state-of-the-art SLAM methods, such as DSO, ORB-SLAM, and LSD-SLAM, both in
terms of accuracy and robustness in trajectory estimation. The proposed method
simultaneously generated significantly detailed 3D geometry from the dense
feature points in real time using only a CPU.
