{"doi":"10.17352/tcsit.000050","title":"OG-SLAM: A real-time and high-accurate monocular visual SLAM framework","abstract":"<jats:p>The challenge of improving the accuracy of monocular Simultaneous Localization and Mapping (SLAM) is considered, which widely appears in computer vision, autonomous robotics, and remote sensing. A new framework (ORB-GMS-SLAM (or OG-SLAM)) is proposed, which introduces the region-based motion smoothness into a typical Visual SLAM (V-SLAM) system. The region-based motion smoothness is implemented by integrating the Oriented Fast and Rotated Brief (ORB) features and the Grid-based Motion Statistics (GMS) algorithm into the feature matching process. The OG-SLAM significantly reduces the absolute trajectory error (ATE) on the key-frame trajectory estimation without compromising the real-time performance. This study compares the proposed OG-SLAM to an advanced V-SLAM system (ORB-SLAM2). The results indicate the highest accuracy improvement of almost 75% on a typical RGB-D SLAM benchmark. Compared with other ORB-SLAM2 settings (1800 key points), the OG-SLAM improves the accuracy by around 20% without losing performance in real-time. The OG-SLAM framework has a significant advantage over the ORB-SLAM2 system in that it is more robust for rotation, loop-free, and long ground-truth length scenarios. Furthermore, as far as the authors are aware, this framework is the first attempt to integrate the GMS algorithm into the V-SLAM.</jats:p>","journal":"Trends in Computer Science and Information Technology","year":2022,"id":664402,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":1,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":378551,"name":"Yuheng Chen","orcid":"0000-0003-0498-4310","position":1,"is_corresponding":false},{"id":1734826,"name":"Zeeshan A Rana","orcid":null,"position":2,"is_corresponding":false},{"id":1734825,"name":"Boyu Kuang","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"OG-SLAM: A real-time and high-accurate monocular visual SLAM framework","abstract":"<jats:p>The challenge of improving the accuracy of monocular Simultaneous Localization and Mapping (SLAM) is considered, which widely appears in computer vision, autonomous robotics, and remote sensing. A new framework (ORB-GMS-SLAM (or OG-SLAM)) is proposed, which introduces the region-based motion smoothness into a typical Visual SLAM (V-SLAM) system. The region-based motion smoothness is implemented by integrating the Oriented Fast and Rotated Brief (ORB) features and the Grid-based Motion Statistics (GMS) algorithm into the feature matching process. The OG-SLAM significantly reduces the absolute trajectory error (ATE) on the key-frame trajectory estimation without compromising the real-time performance. This study compares the proposed OG-SLAM to an advanced V-SLAM system (ORB-SLAM2). The results indicate the highest accuracy improvement of almost 75% on a typical RGB-D SLAM benchmark. Compared with other ORB-SLAM2 settings (1800 key points), the OG-SLAM improves the accuracy by around 20% without losing performance in real-time. The OG-SLAM framework has a significant advantage over the ORB-SLAM2 system in that it is more robust for rotation, loop-free, and long ground-truth length scenarios. Furthermore, as far as the authors are aware, this framework is the first attempt to integrate the GMS algorithm into the V-SLAM.</jats:p>","is_dataset_classified":null,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19965766","pmcid":null,"openalex_id":"https://openalex.org/W4289530584","authors":[],"funders":[],"total_grants":0,"fwci":0.5661,"citation_percentile":0.82626971,"influential_citations":0,"citation_trend":[{"year":2024,"count":1}],"oa_status":"hybrid","license":"cc-by","oa_locations":[{"url":"https://doi.org/10.17352/tcsit.000050","host_type":"journal"},{"url":"https://doi.org/10.17352/tcsit.000050","host_type":"publisher"},{"url":"https://www.engineegroup.com/tcsit/article/download/TCSIT-7-150/pdf","host_type":"publisher"},{"url":"https://www.engineegroup.com/tcsit/article/download/TCSIT-7-150/1826","host_type":"publisher"},{"url":"https://dspace.lib.cranfield.ac.uk/handle/1826/18315","host_type":"repository"}],"fields_of_study":["Robotics and Sensor-Based Localization","Advanced Image and Video Retrieval Techniques","Indoor and Outdoor Localization Technologies"],"mesh_terms":[],"keywords":["Simultaneous localization and mapping","Orb (optics)","Artificial intelligence","Computer vision","Monocular","Computer science","Trajectory","Benchmark (surveying)","Robotics","Key (lock)","RGB color model","Robot","Image (mathematics)","Geography","Mobile robot"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-13T02:43:35.480403Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}