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漂流瓶终于彻底拜拜 微信7.0.4新版体验
微信漂流瓶被玩坏了 聊聊漂流瓶里那些事
微信关闭漂流瓶 它曾经满足了我们对世界的好奇
微信暂停漂流瓶功能:对色情内容零容忍
[视频]惠普Chromebook x360 14 G1评测:搭载Chrome OS的商务变形本
特斯拉:北京客户可三年免息融资购车并免费租赁车牌
借贷宝:停止催收百名裸条女大学生 未满23岁将不得借贷
京东白条多地频现盗刷 消费者遭催收公司“逼债”
借款野蛮催收行为将被规范 真是几家欢喜几家愁
为规范网贷催收 上海互金协会发行业倡议书
腾讯解释为什么微信没有夜间模式 真相你相信吗?
一张发行8年的微信唱片:只收录了4首歌曲


Apple could use machine learning to shore up LiDAR limitations in self-driving
苹果可以使用机器学习来支持自动驾驶的激光雷达限制。

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编辑: 1   作者: Techcrunch   时间: 2018/10/31 17:10:30  

Apple has a new paper published in Cornell's arXiv open directory of scientific research, describing a method for using machine learning to translate the raw point cloud data gathered by LiDAR arrays into results that include detection of 3D objects, including bicycles and pedestrians, with no additional sensor data required.

The paper is one of the clearest looks yet we've had at Apple's work on self-driving technology. We know Apple's working on this because it's had to admit as much in order to secure a self-driving test permit from the California Department of Motor Vehicles, and because its test car has been spotted in and around time.

At the same time, Apple has been opening up a bit more about its machine learning efforts, publishing papers to its own blog highlighting its research, and now also sharing with the broader research community. This kind of publication practice is often a key ingredient for top talent in the field, who hope to work with the broader community to advance ML tech in general.

This specific picture describes how Apple researchers, including paper authors Yin Zhou and Oncel Tuzel, created something called VoxelNet that can extrapolate and infer objects from a collection of points captured by a LiDAR array. Essentially, LiDAR works by creating a high-resolution map of individual points by emitting lasers at its surrounding and registering the reflected results.

The research is interesting because it could allow LiDAR to act much more effectively on its own in self-driving systems. Typically, the LiDAR sensor data is paired or 'fused' with info from optical cameras, radar and other sensors to create a complete picture and perform object detection; using LiDAR alone with a high degree of confidence could lead to future production and computing efficiencies in actual self-driving cars on the road.