基于特征的移动平台单目车辆转换率估计

Michael Gabb, Artem Kaliuk, T. Ruland, O. Löhlein, A. Westenberger, K. Dietmayer
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引用次数: 4

摘要

基于视觉的驾驶员辅助系统在预防死亡事故方面具有巨大潜力。这项工作解决了在需要沿着十字路口和弯道跟踪车辆的情况下的3D单目车辆跟踪和转弯速率估计问题。为了估计履带车辆的转弯速度,采用了一种基于图像特征对应和简化几何车辆模型的方法。该模型使用改进的RANSAC方案对匹配的图像特征进行鲁棒和有效的拟合,该方案自动执行物理上合理的车辆运动,同时加快了整个系统的速度。利用扩展卡尔曼滤波对计算得到的转速进行时域积分,并结合自行车运动模型。实际数据实验证明了所提概念的适用性和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Feature-based monocular vehicle turn rate estimation from a moving platform
Vision-based driver assistance systems have great potential for preventing fatalities. This work addresses the problem of 3D monocular vehicle tracking and turn rate estimation in situations where vehicles need to be tracked along intersections and curves. To estimate the tracked vehicle's turn rate, an approach based on image feature correspondences and a simplified geometric vehicle model is used. The model is robustly and efficiently fitted to the matched image features using an improved RANSAC scheme that automatically enforces physically plausible vehicle motions and speeds up the overall system at the same time. Temporal integration of the computed turn rates is performed by an Extended Kalman Filter with the bicycle motion model. Experiments with real world data show the applicability and robustness of the proposed concepts.
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