Comparison of Major LiDAR Data-Driven Feature Extraction Methods for Autonomous Vehicles

被引：3

作者：

Fernandes, Duarte ^{[1
]}

Nevoa, Rafael ^{[1
]}

Silva, Antonio ^{[1
]}

Simoes, Claudia ^{[2
]}

Monteiro, Joao ^{[1
]}

Novais, Paulo ^{[1
]}

Melo, Pedro ^{[1
,3
]}

机构：

[1] Univ Minho, Algoritmi Ctr, Braga, Portugal

[2] Bosch, Braga, Portugal

[3] Univ Tras Os Montes & Alto Douro, Vila Real, Portugal

来源：

TRENDS AND INNOVATIONS IN INFORMATION SYSTEMS AND TECHNOLOGIES, VOL 2 | 2020年 / 1160卷

关键词：

LiDAR; Point clouds; 3D Object Detection and Classification; CNNs;

D O I：

10.1007/978-3-030-45691-7_54

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Object detection is one of the areas of computer vision that has matured very rapidly. Nowadays, developments in this research area have been playing special attention to the detection of objects in point clouds due to the emerging of high-resolution LiDAR sensors. However, data from a Light Detection and Ranging (LiDAR) sensor is not characterised by having consistency in relative pixel densities and introduces a third dimension, raising a set of drawbacks. The following paper presents a study on the requirements of 3D object detection for autonomous vehicles; presents an overview of the 3D object detection pipeline that generalises the operation principle of models based on point clouds; and categorises the recent works on methods to extract features and summarise their performance.

引用

页码：574 / 583

页数：10

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