# CFP-SLAM: A Real-time Visual SLAM Based on Coarse-to-Fine Probability in Dynamic Environments

arXiv: https://arxiv.org/abs/2202.01938 · HTML: https://mlsanity.com/paper/2202.01938/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2022.

## Built on by (most cited first, 10 of 10)

- [NID-SLAM: Neural Implicit Representation-based RGB-D SLAM in dynamic environments](https://mlsanity.com/paper/2401.01189/) (2024)
- [Semantic Visual Simultaneous Localization and Mapping: A Survey](https://mlsanity.com/paper/2209.06428/) (2022)
- [Learn to Memorize and to Forget: A Continual Learning Perspective of Dynamic SLAM](https://mlsanity.com/paper/2407.13338/) (2024)
- [3DS-SLAM: A 3D Object Detection based Semantic SLAM towards Dynamic Indoor Environments](https://mlsanity.com/paper/2310.06385/) (2023)
- [V3D-SLAM: Robust RGB-D SLAM in Dynamic Environments with 3D Semantic Geometry Voting](https://mlsanity.com/paper/2410.12068/) (2024)
- [NGD-SLAM: Towards Real-Time Dynamic SLAM without GPU](https://mlsanity.com/paper/2405.07392/) (2024)
- [An Earth Rover dataset recorded at the ICRA@40 party](https://mlsanity.com/paper/2407.05735/) (2024)
- [GeneA-SLAM2: Dynamic SLAM with AutoEncoder-Preprocessed Genetic Keypoints Resampling and Depth Variance-Guided Dynamic Region Removal](https://mlsanity.com/paper/2506.02736/) (2025)
- [Proactive Scene Decomposition and Reconstruction](https://mlsanity.com/paper/2510.16272/) (2025)
- [DL-SLAM: Enabling High-Fidelity Gaussian Splatting SLAM in Dynamic Environments based on Dual-Level Probability](https://mlsanity.com/paper/2607.01860/) (2026)

## Abstract (arXiv)

The dynamic factors in the environment will lead to the decline of camera localization accuracy due to the violation of the static environment assumption of SLAM algorithm. Recently, some related works generally use the combination of semantic constraints and geometric constraints to deal with dynamic objects, but problems can still be raised, such as poor real-time performance, easy to treat people as rigid bodies, and poor performance in low dynamic scenes. In this paper, a dynamic scene-oriented visual SLAM algorithm based on object detection and coarse-to-fine static probability named CFP-SLAM is proposed. The algorithm combines semantic constraints and geometric constraints to calculate the static probability of objects, keypoints and map points, and takes them as weights to participate in camera pose estimation. Extensive evaluations show that our approach can achieve almost the best results in high dynamic and low dynamic scenarios compared to the state-of-the-art dynamic SLAM methods, and shows quite high real-time ability.