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Using CART to Segment Road Images

机译:使用CART分割道路图像

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摘要

The 2005 DARPA Grand Challenge is a 132 mile race through the desert with autonomous robotic vehicles. Lasers mounted on the car roof provide a map of the road up to 20 meters ahead of the car but the car needs to see further in order to go fast enough to win the race. Computer vision can extend that map of the road ahead but desert road is notoriously similar to the surrounding desert. The CART algorithm (Classification and Regression Trees) provided a machine learning boost to find road while at the same time measuring when that road could not be distinguished from surrounding desert.
机译:2005年DARPA大挑战赛是在132英里长的沙漠中通过自动驾驶机器人进行的比赛。安装在车顶上的激光灯可提供距汽车前方20米的道路图,但汽车需要进一步观察才能快速行驶以赢得比赛。计算机视觉可以扩展前方的道路地图,但众所周知,沙漠公路与周围的沙漠相似。 CART算法(分类树和回归树)为查找道路提供了机器学习的助力,同时测量了何时无法将该道路与周围的沙漠区分开。

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