用事件相机提升动态环境下的定位精度
PLED-VINS: A Point-Line Event-Based Visual Inertial SLAM for Dynamic Environments
- 基于事件相机的时空统计，评估点线特征的可靠性
- 融合时序与几何可靠性，显著降低动态物体干扰
- 适合高动态场景下的实时定位，如自动驾驶
动态环境仍是视觉SLAM的核心挑战，移动物体导致的不可靠观测和快速运动会降低状态估计精度。尽管事件相机能保留精细的时空信息，但现有事件相机SLAM框架多假设场景静态，缺乏对特征可靠性的评估方法。为此，我们提出PLED-VINS，一种基于单目事件相机的视觉惯性SLAM框架，可在动态环境中实现鲁棒的状态估计。通过熵-时效评分图，基于事件时间统计刻画点与线特征的时序可靠性；同时，通过统一的点线鲁棒捆绑调整估计几何可靠性。在此基础上，设计自适应加权策略，融合时序与几何可靠性，包含针对线特征的运动条件可靠性建模，以抑制不可靠观测。实验结果表明，PLED-VINS在含移动物体的动态序列上显著提升了状态估计性能。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。