ICD-Net：用于无人机视觉-惯性SLAM的惯性协方差位移网络
ICD-Net: Inertial Covariance Displacement Network for Drone Visual-Inertial SLAM
AI总结：
ICD-Net通过学习处理原始惯性数据并生成带有不确定性的位移估计，提升无人机视觉-惯性SLAM的鲁棒性和准确性。
摘要：
视觉-惯性SLAM系统常常由于多个混淆因素导致性能欠佳，包括不完美的传感器校准、噪声测量、快速运动动态、低光照条件以及传统惯性导航集成方法的固有限制。这些问题在无人机应用中尤为突出，因为鲁棒且准确的状态估计对于安全的自主操作至关重要。在本工作中，我们提出了ICD-Net，一种新的框架，通过学习处理原始惯性测量并生成带有相关不确定性量化的位移估计来提升视觉-惯性SLAM性能。与依赖于分析惯性传感器模型而难以应对现实世界传感器不完美问题的方法不同，我们的方法直接从传感器数据中提取位移图，同时预测反映估计信心的测量协方差。我们将ICD-Net的输出作为额外残差约束整合到VINS-Fusion优化框架中，其中预测的不确定性适当地加权神经网络贡献相对于传统视觉和惯性项。所学习的位移约束提供了互补信息，补偿SLAM流水线中的各种误差源。我们的方法可以在正常运行条件和摄像头不一致或视觉退化的情况下使用。在具有挑战性的高速无人机序列上的实验评估表明，我们的方法在轨迹估计准确性方面显著优于标准VINS-Fusion，平均APE的改进超过38%，不确定性估计证明对于维持系统鲁棒性至关重要。我们的方法表明，神经网络增强可以有效解决多个SLAM退化源，同时保持实时性能要求。
英文摘要：
Visual-inertial SLAM systems often exhibit suboptimal performance due to multiple confounding factors including imperfect sensor calibration, noisy measurements, rapid motion dynamics, low illumination, and the inherent limitations of traditional inertial navigation integration methods. These issues are particularly problematic in drone applications where robust and accurate state estimation is critical for safe autonomous operation. In this work, we present ICD-Net, a novel framework that enhances visual-inertial SLAM performance by learning to process raw inertial measurements and generating displacement estimates with associated uncertainty quantification. Rather than relying on analytical inertial sensor models that struggle with real-world sensor imperfections, our method directly extracts displacement maps from sensor data while simultaneously predicting measurement covariances that reflect estimation confidence. We integrate ICD-Net outputs as additional residual constraints into the VINS-Fusion optimization framework, where the predicted uncertainties appropriately weight the neural network contributions relative to traditional visual and inertial terms. The learned displacement constraints provide complementary information that compensates for various error sources in the SLAM pipeline. Our approach can be used under both normal operating conditions and in situations of camera inconsistency or visual degradation. Experimental evaluation on challenging high-speed drone sequences demonstrated that our approach significantly improved trajectory estimation accuracy compared to standard VINS-Fusion, with more than 38% improvement in mean APE and uncertainty estimates proving crucial for maintaining system robustness. Our method shows that neural network enhancement can effectively address multiple sources of SLAM degradation while maintaining real-time performance requirements.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
