The article contains an analysis of potential prospects of simultaneous localization and mapping (SLAM) algorithms application in imagery intelligence (IMINT). The first part of the paper presents a detailed description of the SLAM problem. Diverse solutions to the simultaneous localization and mapping problem and related research over the years are presented. The most promising of SLAM approaches are pointed out. To facilitate SLAM analysis, the problem is partitioned into three parts. First, various SLAM estimation techniques are characterized. A mathematical theory behind the usage of parametric filters, non-parametric filters, and least squares method is presented. Further, differences between SLAM algorithms are described in terms of various sensors used on-board SLAM platforms for the examination of the environment. The examination is commonly addressed as landmark extraction. A separate part of the paper discusses the image processing in SLAM. The last part of the SLAM analysis is dedicated to various approaches to map presentation. Further, the properties of SLAM techniques are characterized in terms of their potential benefits to IMINT. Prospects of increased efficiency, accuracy and safety of intelligence gathering process are discussed.