# GeneA-SLAM2: Dynamic SLAM with AutoEncoder-Preprocessed Genetic Keypoints Resampling and Depth Variance-Guided Dynamic Region Removal

arXiv: https://arxiv.org/abs/2506.02736 · HTML: https://mlsanity.com/paper/2506.02736/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 3 Jun 2025.

## Builds on (most cited first, 11 of 11)

- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals](https://mlsanity.com/paper/1905.02082/) (2019)
- [DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes](https://mlsanity.com/paper/1806.05620/) (2018)
- [DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments](https://mlsanity.com/paper/1809.08379/) (2018)
- [DG-SLAM: Robust Dynamic Gaussian Splatting SLAM with Hybrid Pose Optimization](https://mlsanity.com/paper/2411.08373/) (2024)
- [RoDyn-SLAM: Robust Dynamic Dense RGB-D SLAM with Neural Radiance Fields](https://mlsanity.com/paper/2407.01303/) (2024)
- [CFP-SLAM: A Real-time Visual SLAM Based on Coarse-to-Fine Probability in Dynamic Environments](https://mlsanity.com/paper/2202.01938/) (2022)
- [Towards Real-time Semantic RGB-D SLAM in Dynamic Environments](https://mlsanity.com/paper/2104.01316/) (2021)
- [PLE-SLAM: A Visual-Inertial SLAM Based on Point-Line Features and Efficient IMU Initialization](https://mlsanity.com/paper/2401.01081/) (2024)
- [NGD-SLAM: Towards Real-Time Dynamic SLAM without GPU](https://mlsanity.com/paper/2405.07392/) (2024)
- [D2SLAM: Semantic visual SLAM based on the Depth-related influence on object interactions for Dynamic environments](https://mlsanity.com/paper/2210.08647/) (2022)

## Abstract (arXiv)

Existing semantic SLAM in dynamic environments mainly identify dynamic regions through object detection or semantic segmentation methods. However, in certain highly dynamic scenarios, the detection boxes or segmentation masks cannot fully cover dynamic regions. Therefore, this paper proposes a robust and efficient GeneA-SLAM2 system that leverages depth variance constraints to handle dynamic scenes. Our method extracts dynamic pixels via depth variance and creates precise depth masks to guide the removal of dynamic objects. Simultaneously, an autoencoder is used to reconstruct keypoints, improving the genetic resampling keypoint algorithm to obtain more uniformly distributed keypoints and enhance the accuracy of pose estimation. Our system was evaluated on multiple highly dynamic sequences. The results demonstrate that GeneA-SLAM2 maintains high accuracy in dynamic scenes compared to current methods. Code is available at: https://github.com/qingshufan/GeneA-SLAM2.