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Monocular visual odometry (VO) and simultaneous localization and mapping(SLAM) have seen tremendous improvements in accuracy, robustness andefficiency, and have gained increasing popularity over recent years.Nevertheless, not so many discussions have been carried out to reveal theinfluences of three very influential yet easily overlooked aspects: photometriccalibration, motion bias and rolling shutter effect. In this work, we evaluatethese three aspects quantitatively on the state of the art of direct,feature-based and semi-direct methods, providing the community with usefulpractical knowledge both for better applying existing methods and developingnew algorithms of VO and SLAM. Conclusions (some of which arecounter-intuitive) are drawn with both technical and empirical analyses to allof our experiments. Possible improvements on existing methods are directed orproposed, such as a sub-pixel accuracy refinement of ORB-SLAM which boosts itsperformance.

Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias and Rolling Shutter Effect