3D Point Cloud Processing and Learning for Autonomous Driving: Impacting Map Creation, Localization, and Perception

Siheng Chen, Baoan Liu, Chen Feng, Carlos Vallespi-Gonzalez, Carl Wellington

Research output: Contribution to journalArticlepeer-review

Abstract

We present a review of 3D point cloud processing and learning for autonomous driving. As one of the most important sensors in autonomous vehicles (AVs), lidar sensors collect 3D point clouds that precisely record the external surfaces of objects and scenes. The tools for 3D point cloud processing and learning are critical to the map creation, localization, and perception modules in an AV. Although much attention has been paid to data collected from cameras, such as images and videos, an increasing number of researchers have recognized the importance and significance of lidar in autonomous driving and have proposed processing and learning algorithms that exploit 3D point clouds. We review the recent progress in this research area and summarize what has been tried and what is needed for practical and safe AVs. We also offer perspectives on open issues that are needed to be solved in the future.

Original languageEnglish (US)
Article number9307334
Pages (from-to)68-86
Number of pages19
JournalIEEE Signal Processing Magazine
Volume38
Issue number1
DOIs
StatePublished - Jan 2021

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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