PLED-VINS: A Point-Line Event-Based Visual Inertial SLAM for Dynamic Environments

• URL: http://arxiv.org/abs/2607.07374v1
• Date: Wed, 08 Jul 2026 13:06:13 GMT
• Title: PLED-VINS: A Point-Line Event-Based Visual Inertial SLAM for Dynamic Environments
• Abstract summary: We propose PLED-VINS, a visual-inertial SLAM framework that enables robust state estimation in dynamic environments.<n>We show that PLED-VINS improves state estimation on the evaluated dynamic sequences moving with objects.
• Score: 8.963834719587911
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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