用深度方差和自编码器优化关键点，提升动态环境下的定位精度
GeneA-SLAM2: Dynamic SLAM with AutoEncoder-Preprocessed Genetic Keypoints Resampling and Depth Variance-Guided Dynamic Region Removal
- 通过深度方差检测动态像素，生成精确深度掩码
- 自编码器重构关键点，使重采样更均匀，提升位姿估计准确率
- 适合高动态场景的SLAM系统，尤其在遮挡或快速运动下表现稳健
现有语义SLAM在动态环境中主要依赖目标检测或语义分割识别动态区域，但在高度动态场景中，检测框或分割掩码常无法完全覆盖动态物体。本文提出鲁棒高效的GeneA-SLAM2系统，利用深度方差约束处理动态场景。方法通过深度方差提取动态像素，生成精确深度掩码，指导动态物体移除；同时引入自编码器重构关键点，改进遗传重采样算法，获得更均匀分布的关键点，提升位姿估计精度。系统在多个高度动态序列上评估，结果表明相比现有方法，GeneA-SLAM2在动态场景中保持更高定位精度。代码已开源：https://github.com/qingshufan/GeneA-SLAM2。
原文摘要 · Abstract (English)
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.
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