标题： GeneA-SLAM2：具有自动编码器预处理遗传关键点重采样和深度方差引导的动态区域去除的动态SLAM 显示英文标题

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

摘要： 现有的语义SLAM在动态环境中的主要方法是通过物体检测或语义分割来识别动态区域。然而，在某些高度动态的场景中，检测框或分割掩码无法完全覆盖动态区域。因此，本文提出了一种鲁棒且高效的GeneA-SLAM2系统，利用深度变化约束来处理动态场景。我们的方法通过深度变化提取动态像素，并创建精确的深度掩码以指导移除动态对象。同时，使用自动编码器重建关键点，改进遗传重采样关键点算法，从而获得更均匀分布的关键点并提高姿态估计的准确性。我们的系统在多个高度动态的序列上进行了评估。结果显示，与现有方法相比，GeneA-SLAM2在动态场景中保持了更高的准确性。代码可在以下地址获取：https://github.com/qingshufan/GeneA-SLAM2。 显示英文摘要

摘要： 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.