{"ID":6138245,"CreatedAt":"2026-07-09T01:07:32.349475501Z","UpdatedAt":"2026-07-11T12:58:56.760471157Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.07374","arxiv_id":"2607.07374","title":"PLED-VINS: A Point-Line Event-Based Visual Inertial SLAM for Dynamic Environments","abstract":"Dynamic environments remain a fundamental challenge for visual SLAM, where unreliable observations from moving objects and rapid motion degrade state estimation accuracy. Although event cameras preserve fine-grained spatio-temporal information, most existing event-based SLAM frameworks still assume static scenes and lack approaches to estimate the reliability of features. To this end, we propose PLED-VINS, a monocular event camera-based visual-inertial SLAM framework that enables robust state estimation in dynamic environments. We propose an entropy-recency score map to characterize the temporal reliability of both point and line features based on event temporal statistics. Concurrently, geometric reliability is estimated via a unified point-line robust bundle adjustment. Building upon these, we design an adaptive weighting strategy that fuses temporal and geometric reliability, including motion-conditioned reliability modeling for line features, to suppress unreliable observations. Experimental results demonstrate that PLED-VINS improves state estimation on the evaluated dynamic sequences with moving objects.","short_abstract":"Dynamic environments remain a fundamental challenge for visual SLAM, where unreliable observations from moving objects and rapid motion degrade state estimation accuracy. Although event cameras preserve fine-grained spatio-temporal information, most existing event-based SLAM frameworks still assume static scenes and la...","url_abs":"https://arxiv.org/abs/2607.07374","url_pdf":"https://arxiv.org/pdf/2607.07374v1","authors":"[\"Seunghun Lee\",\"Jihun Nam\",\"Dong-Uk Seo\",\"Hyun Myung\"]","published":"2026-07-08T13:06:13Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}