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

TL;DR

GeneA-SLAM2 introduces a robust dynamic SLAM approach utilizing depth variance and autoencoder-based keypoint reconstruction to improve accuracy in highly dynamic environments.

Contribution

It combines depth variance constraints with autoencoder-enhanced genetic keypoints resampling for improved dynamic scene SLAM performance.

Findings

Maintains high accuracy in highly dynamic sequences.

Outperforms existing methods in dynamic scene scenarios.

Effectively removes dynamic regions using depth variance masks.

Abstract

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.