Monocular visual odometry (VO) and simultaneous localization and mapping
(SLAM) have seen tremendous improvements in accuracy, robustness and
efficiency, and have gained increasing popularity over recent years.
Nevertheless, not so many discussions have been carried out to reveal the
influences of three very influential yet easily overlooked aspects: photometric
calibration, motion bias and rolling shutter effect. In this work, we evaluate
these three aspects quantitatively on the state of the art of direct,
feature-based and semi-direct methods, providing the community with useful
practical knowledge both for better applying existing methods and developing
new algorithms of VO and SLAM. Conclusions (some of which are
counter-intuitive) are drawn with both technical and empirical analyses to all
of our experiments. Possible improvements on existing methods are directed or
proposed, such as a sub-pixel accuracy refinement of ORB-SLAM which boosts its
performance.

通过定量评估直接、特征和半直接方法的最新技术，提供了社区有用的实用知识，以便更好地应用现有的方法和开发新的 VO 和 SLAM 算法，并提出了可能的改进现有方法的建议。