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

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

Speckle tracking approaches in speckle sensing

This paper reports some initial investigations into the application of feature tracking algorithms as an alternative data processing method for speckle correlation sensors capable of determining both the speckle pattern translation and rotation. The accuracy of translation measurements using the feature tracking approach was found to be similar to that of correlation based processing with accuracies of < 0.04 pixels. Rotation measurement accuracies of< 0.05 ◦ are found to be achievable over angle range ±20 ◦ , limited by the failure to match speckles at larger rotation angles