GeneA-SLAM2: オートエンコーダ前処理と遺伝的キーポイント再サンプリング、深度分散誘導の動的領域除去による動的SLAM
GeneA-SLAM2: Dynamic SLAM with AutoEncoder-Preprocessed Genetic Keypoints Resampling and Depth Variance-Guided Dynamic Region Removal
深度分散で動的領域を検出・除去し、オートエンコーダでキーポイントを再構成して遺伝的再サンプリングを改善することで、動的環境でも高精度な自己位置推定を実現したSLAMシステム。
著者: Shufan Qing, Anzhen Li, Qiandi Wang, Yuefeng Niu, Mingchen Feng, Guoliang Hu, Jinqiao Wu, Fengtao Nan, Yingchun Fan
分類: cs.CV, cs.RO
原文アブストラクト
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.