{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/genea-slam2-dynamic-slam-with-autoencoder","title":"GeneA-SLAM2: Dynamic SLAM with AutoEncoder-Preprocessed Genetic Keypoints Resampling and Depth Variance-Guided Dynamic Region Removal","arxiv_id":"2506.02736","date":"2025-06-03","proceeding":null,"authors":["Shufan Qing","Anzhen Li","Qiandi Wang","Yuefeng Niu","Mingchen Feng","Guoliang Hu","Jinqiao Wu","Fengtao Nan","Yingchun Fan"],"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.","url_abs":"https://arxiv.org/abs/2506.02736v1","url_pdf":"https://arxiv.org/pdf/2506.02736v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"genea-slam2-dynamic-slam-with-autoencoder","repo_url":"https://github.com/qingshufan/GeneA-SLAM2","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"semantic-slam","task_name":"Semantic SLAM"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"orb-slam2","method_name":"ORB-SLAM2"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-slam-on-bonn-rgb-d-dynamic","task":"Semantic SLAM","dataset":"Bonn RGB-D Dynamic","model":"GeneA-SLAM2 synchronous2","rank_in_archive_order":1,"of":1,"metrics":{"ATE":"0.008"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-slam-on-tum-rgb-d","task":"Semantic SLAM","dataset":"TUM RGB-D","model":"GeneA-SLAM2 f3/w/static","rank_in_archive_order":1,"of":1,"metrics":{"ATE":"0.007"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}