Keyframe-based monocular SLAM: design, survey, and future directions

TL;DR

This paper surveys keyframe-based monocular SLAM systems, offering design guidelines, analyzing existing methods, and discussing future research directions to overcome current limitations like illumination changes and dynamic scenes.

Contribution

It provides a comprehensive survey of keyframe-based monocular SLAM, offers design guidelines for different environments, and discusses future research challenges and directions.

Findings

Keyframe-based SLAM is the dominant methodology in monocular systems.

The survey details various implementation strategies and their effectiveness.

Future research should focus on robustness to illumination, dynamic scenes, and map maintenance.

Abstract

Extensive research in the field of monocular SLAM for the past fifteen years has yielded workable systems that found their way into various applications in robotics and augmented reality. Although filter-based monocular SLAM systems were common at some time, the more efficient keyframe-based solutions are becoming the de facto methodology for building a monocular SLAM system. The objective of this paper is threefold: first, the paper serves as a guideline for people seeking to design their own monocular SLAM according to specific environmental constraints. Second, it presents a survey that covers the various keyframe-based monocular SLAM systems in the literature, detailing the components of their implementation, and critically assessing the specific strategies made in each proposed solution. Third, the paper provides insight into the direction of future research in this field, to address the major limitations still facing monocular SLAM; namely, in the issues of illumination changes, initialization, highly dynamic motion, poorly textured scenes, repetitive textures, map maintenance, and failure recovery.