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

arXiv: https://arxiv.org/abs/1607.00470 · HTML: https://mlsanity.com/paper/1607.00470/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2016.

## Built on by (most cited first, 13 of 13)

- [SSCBench: A Large-Scale 3D Semantic Scene Completion Benchmark for Autonomous Driving](https://mlsanity.com/paper/2306.09001/) (2023)
- [A Large Scale Homography Benchmark](https://mlsanity.com/paper/2302.09997/) (2023)
- [KeySG: Hierarchical Keyframe-Based 3D Scene Graphs](https://mlsanity.com/paper/2510.01049/) (2025)
- [Semantic Visual Simultaneous Localization and Mapping: A Survey](https://mlsanity.com/paper/2209.06428/) (2022)
- [Improved Real-Time Monocular SLAM Using Semantic Segmentation on Selective Frames](https://mlsanity.com/paper/2105.00114/) (2021)
- [Monocular visual simultaneous localization and mapping: (r)evolution from geometry to deep learning-based pipelines](https://mlsanity.com/paper/2503.02955/) (2025)
- [Keyframe-Based Feed-Forward Visual Odometry](https://mlsanity.com/paper/2601.16020/) (2026)
- [HDPV-SLAM: Hybrid Depth-augmented Panoramic Visual SLAM for Mobile Mapping System with Tilted LiDAR and Panoramic Visual Camera](https://mlsanity.com/paper/2301.11823/) (2023)
- [Comparison of Various SLAM Systems for Mobile Robot in an Indoor Environment](https://mlsanity.com/paper/2501.09490/) (2025)
- [BREATH-VL: Vision-Language-Guided 6-DoF Bronchoscopy Localization via Semantic-Geometric Fusion](https://mlsanity.com/paper/2601.03713/) (2026)
- [Motion-Bias-Free Feature-Based SLAM](https://mlsanity.com/paper/2309.06792/) (2023)
- [Solving Short-Term Relocalization Problems In Monocular Keyframe Visual SLAM Using Spatial And Semantic Data](https://mlsanity.com/paper/2407.19518/) (2024)
- [Lost in Tracking Translation: A Comprehensive Analysis of Visual SLAM in Human-Centered XR and IoT Ecosystems](https://mlsanity.com/paper/2411.07146/) (2024)

## Abstract (arXiv)

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.