  Visual Odometry (VO) can be categorized as being either direct or feature
based. When the system is calibrated photometrically, and images are captured
at high rates, direct methods have shown to outperform feature-based ones in
terms of accuracy and processing time; they are also more robust to failure in
feature-deprived environments. On the downside, Direct methods rely on
heuristic motion models to seed the estimation of camera motion between frames;
in the event that these models are violated (e.g., erratic motion), Direct
methods easily fail. This paper proposes a novel system entitled FDMO (Feature
assisted Direct Monocular Odometry), which complements the advantages of both
direct and featured based techniques. FDMO bootstraps indirect feature tracking
upon the sub-pixel accurate localized direct keyframes only when failure modes
(e.g., large baselines) of direct tracking occur. Control returns back to
direct odometry when these conditions are no longer violated. Efficiencies are
introduced to help FDMO perform in real time. FDMO shows significant drift
(alignment, rotation & scale) reduction when compared to DSO & ORB SLAM when
evaluated using the TumMono and EuroC datasets.
