An Efficient Scan-to-Map Matching Approach Based on Multi-channel Lidar

被引：6

作者：

Fu, Hao ^{[1
]}

Yu, Rui ^{[1
]}

Ye, Lei ^{[1
]}

Wu, Tao ^{[1
]}

Xu, Xin ^{[1
]}

机构：

[1] Natl Univ Def Technol, Coll Mechatron Engn & Automat, Changsha 410073, Hunan, Peoples R China

来源：

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS | 2018年 / 91卷 / 3-4期

基金：

中国国家自然科学基金;

关键词：

Mapping and localization; Autonomous driving;

D O I：

10.1007/s10846-017-0717-0

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Accurately localizing the vehicle against a pre-built high precision map is an essential step for the Autonomous Land Vehicle (ALV). This paper proposes an efficient scan-to-map matching approach based on multi-channel lidar. We firstly advocate the usage of both the lidar reflectance map and the height map, as these two maps contain complementary information. Then, borrowing ideas from the Lucas-Kanade optical flow approach, we formulate the scan-to-map matching problem in a similar form, and propose an efficient gradient descent approach to solve it. Finally, the proposed approach is integrated into a filtering framework for real-time online localization. Experiments on real-world dataset have demonstrated the validity of our approach.

引用

页码：501 / 513

页数：13