One of the most significant challenges in sustainable autonomous mobile robot and vehicle development is the perception of stochastic environments. Various environmental perception methods have been proposed to address these challenges; however, these methods often lack general applicability. Many of these methods rely on environmental feature extraction, which can fail in specific scenarios, such as monochromatic environments. This article aims to evaluate existing SLAM (Simultaneous Localization and Mapping) methods that utilize camera or combined camera and LiDAR input data in predominantly monochromatic environments. Additionally, this study seeks to identify performance issues in such applications.