Studying the Effect of Preprocessing on Simultaneous Localization and Mapping in Low-Light Conditions

Low light conditions make computer vision practices difficult. Although the current simultaneous localization and mapping methods are mature, it is difficult to have accurate maps with challenging low light conditions like the ones encountered when dealing with underwater images. This paper studies the effect of using preprocessing to improve the performance and accuracy of simultaneous localization and mapping that focuses on low-light scenes, especially those taken underwater. More specifically, the paper compares the effect of classical and deep learning preprocessing approaches. After hyperparameter tuning, the classical contrast limited adaptive histogram equalization approach is found to achieve the best results with a 20.74% increase in accuracy on the Aqualoc underwater dataset.