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Abstract

AVP服务会缓和电动车现有两个缺点: 有限的行驶范围和很长的充电时间.

v-charge用相机和超声波在GPS-denied的区域全自动形式. 这篇paper叙述了下述几方面的优势:

  • network communication
  • parking space scheduling
  • multi-camera calibration
  • semantic mapping concepts
  • visual localization
  • motion planning

这个项目推动了视觉定位, 环境感知和自动泊车到厘米级别的精度.

研发的infrastructure-based camera calibration, semi-supervised semantic mapping concepts极大的减少了维护的成本.

1. Introduction

只用了4个鱼眼相机, 两个stereo相机和超声波雷达.

2. Platform and Sensor Setup

3. Multi-Camera Calibration

developed unsupervised, highly accurate calibration methods for the surround view camera system. the calibration method makes use of natural features in the environment to minimise infrastructure setup costs.

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4. Offline Mapping

用SfM的方法离线建图. 每一个3D有额外的descriptors from all images.

5. Perception

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用SfM pipeline来全方位.

A. Motion Stereo / Structure from Motion

用plane sweeping.

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B. Occupancy Grid Map Fusion

6. Semantic Mapping

sec4 建立了一个metric layer of the map stack.

这里用semantic layer扩展了map stack, 其中有三个特别的部分:

  • a road graph
  • parking space的位置
  • a speed profile

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A. The Road Graph

通过metric layer计算的pose组成了lanes.

B. The Parking Labels

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C. Speed Map

创建了额一个probabilistic graphical model用路线的位置和parking space作为先验.

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7. Communication and Scheduling

8. Visual Localisation

在drop-off位置开始已定位. 定位只用了单目的相机和自然特征.

会用不同时间和日子的地图来augment地图. 要重复这个步骤.

9. Object Detection and Classification

10. Motion Planning

11. Conclusion

posted on 2019-12-17 10:30  四积阴功五读书  阅读(98)  评论(0编辑  收藏