具有高效ConvNet特征的视觉导航

H. Jaspers, Dennis Fassbender, Hans-Joachim Wünsche
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引用次数: 2

摘要

本文提出了一种自动驾驶车辆无视线跟随系统。前车从单目摄像机图像中提取场景描述符,并通过车对车(V2V)通信传输给后车。追随者能够使用自己的相机识别场景并自主跟随。采用粒子滤波框架对前车驱动路径进行无跳定位。我们比较了不同地点特征在自定义面向应用的数据集上的准确定位性能,并评估了在保持甚至提高识别性能的同时减少低带宽V2V通信特征尺寸的方法。实际结果证明了系统的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visual navigation with efficient ConvNet features
In this paper, we propose a system for autonomous vehicle following without a line of sight. From monocular camera images, the leading vehicle extracts scene descriptors which it transmits to the following vehicle by means of vehicle-to-vehicle (V2V) communication. The follower is able to recognize the scenes using its own camera and follow autonomously. A particle filter framework is employed for jump-free localization on the driven path of the leading vehicle. We compare the performance of different place features for accurate localization on a custom application-oriented dataset and evaluate methods to reduce the feature size for low-bandwidth V2V communication, while maintaining and even improving the recognition performance. Real-world results demonstrate the applicability of our system.
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