基于事件相机的定位与建图算法: 综述

马艳阳; 叶梓豪; 刘坤华; 陈龙

doi:10.16383/j.aas.c190550

基于事件相机的定位与建图算法: 综述

doi: 10.16383/j.aas.c190550

马艳阳^{1, 2,},
叶梓豪^{1, 2,},
刘坤华^{1, 2,},
陈龙^{1, 2,}

1.
中山大学数据科学与计算机学院广州 510006
2.
中山大学无人系统研究所广州 510006

基金项目: 国家重点研发计划(2018YFB1305002), 国家自然科学基金(61773414)资助

详细信息

作者简介:
马艳阳：2020年获得中山大学硕士学位. 2018年获得中山大学计算机科学与技术学士学位. 主要研究方向为机器人定位与建图技术. E-mail: mayany3@mail2.sysu.edu.cn

叶梓豪：中山大学计算机学院硕士研究生. 2020年获得中山大学软件工程学士学位. 主要研究方向为多传感器融合的即时同步定位与建图技术. E-mail: yezh9@mail2.sysu.edu.cn

刘坤华：中山大学数据科学与计算机学院博士后. 2019年获得山东科技大学机电工程学院博士学位. 主要研究方向为自动驾驶环境感知. E-mail: lkhzyf@163.com

陈龙：中山大学数据科学与计算机学院副教授. 于2007年、2013年获得武汉大学学士、博士学位. 主要研究方向为自动驾驶, 机器人, 人工智能. 本文通信作者. E-mail: chenl46@mail.sysu.edu.cn

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出版历程
- 收稿日期: 2019-07-25
- 录用日期: 2019-12-15
- 网络出版日期: 2020-01-03
- 刊出日期: 2021-07-27

Event-based Visual Localization and Mapping Algorithms: A Survey

MA Yan-Yang^{1, 2
,},
YE Zi-Hao^{1, 2
,},
LIU Kun-Hua^{1, 2
,},
CHEN Long^{1, 2
,}

1.
School of Data and Computer Science, Sun Yat-sen Univercity, Guangzhou 510006
2.
Institute of Unmanned Systems, Sun Yat-sen Univercity, Guangzhou 510006

Funds: Supported by National Key Research and Development Program of China (2018YFB1305002), National Natural Science Foundation of China (61773414)

More Information

Author Bio:
MA Yan-Yang　He received his master degree from the School of Computer Science and Engineering, Sun Yat-Sen University in 2020. He received his bachelor degree from Sun Yat-Sen University in 2018. His research interest covers robot localization and mapping

YE Zi-Hao　Master student at the School of Computer Science and Engineering, Sun Yat-Sen University. He received his bachelor degree in software engineering from Sun Yat-Sen University in 2020. His research interest covers real-time synchronous localization and mapping technology of multi-sensor fusion

LIU Kun-Hua　Postdoctor at the School of Data and Computer Science, Sun Yat-sen University. She received her Ph.D. degree from the Mechanical and Electrical Engineering Institute, Shandong University of Science and Technology. Her research interest covers automatic driving environment perception

CHEN Long　Associate professor at the School of Data and Computer Science, Sun Yat-sen University. He received his bachelor degree and Ph.D. degree from Wuhan University in 2007 and 2013. His research interest covers autonomous driving, robotics and artificial intelligence. Corresponding author of this paper

摘要

摘要:
事件相机是一种新兴的视觉传感器, 通过检测单个像素点光照强度的变化来产生“事件”. 基于其工作原理, 事件相机拥有传统相机所不具备的低延迟、高动态范围等优良特性. 而如何应用事件相机来完成机器人的定位与建图则是目前视觉定位与建图领域新的研究方向. 本文从事件相机本身出发, 介绍事件相机的工作原理、现有的定位与建图算法以及事件相机相关的开源数据集. 其中, 本文着重对现有的、基于事件相机的定位与建图算法进行详细的介绍和优缺点分析.
- 事件相机 /
- 低延迟 /
- 位姿估计 /
- 定位与建图
Abstract:
Event-based camera is a new type of visual sensor, which activates “events” by monitoring the changes of lighting intensity. Event camera offers low-latency output and tolerates high dynamic range, therefore, it is a new topic in the SLAM area applying event-based camera to robot localization and mapping. This work introduces the working principle of event-based camera and reviews present algorithms and dataset of event-based localization and mapping. Emphasis of this work is introducing and analysing event-based localization and mapping algorithms.
- Event camera /
- low latency /
- pose estimation /
- localization and mapping

HTML全文

图 1 事件相机输出的地址−事件流^[47]

Fig. 1 Address-event stream output by event-based camera^[47]

下载: 全尺寸图片幻灯片

图 2 DVS像素结构原理图^[34]

Fig. 2 Abstracted DVS pixel core schematic^[34]

下载: 全尺寸图片幻灯片

图 3 DVS工作原理图^[34]

Fig. 3 Principle of DVS operation^[34]

下载: 全尺寸图片幻灯片

图 4 Bryner算法工作流程^[51]

Fig. 4 The workflow of Bryner＇ s algorithm^[51]

下载: 全尺寸图片幻灯片

表 1 文中叙述的部分基于事件相机的SLAM算法及应用

Table 1 Event-based SLAM algorithms and applications

相关文献	所使用传感器	维度	算法类型	是否需要输入地图	发表时间 (年)
[44]	DVS	2D	定位	是	2012
[45]	DVS	2D	定位与建图	否	2013
[47]	DVS	3D	定位	是	2014
[48]	DVS	3D	定位与建图	否	2016
[49]	DVS	3D	定位与建图	否	2016
[51]	DVS	3D	定位	是	2019
[52]	DVS, 灰度相机	3D	定位	否	2014
[53]	DVS, RGB-D相机	3D	定位与建图	否	2014
[55]	DAVIS	3D	定位	否	2016
[56]	DAVIS (内置IMU)	3D	定位	否	2017
[59]	DAVIS (内置IMU)	3D	定位与建图	否	2017
[64]	DAVIS (内置IMU), RGB相机	3D	定位与建图	否	2018
[65]	DAVIS (内置IMU)	3D	定位	否	2018

下载: 导出CSV

表 2 DVS公开数据集

Table 2 Dataset provided by event cammera

相关文献	所使用传感器	相机运动自由度	数据采集场景	载具	是否提供真值	发表时间(年)
[53]	eDVS相机, RGB-D相机	6DOF	室内	手持	是	2014
[28]	DAVIS (内置IMU)	3DOF(纯旋转)	室内, 仿真	旋转基座	是	2016
[68]	DAVIS, RGB-D相机	4DOF	室内, 仿真	地面机器人和云台	是	2016
[69]	DAVIS (内置IMU)	6DOF	室内室外仿真	手持	室内: 是室外: 否仿真: 是	2016
[70]	DAVIS	6DOF	室外	汽车	是	2017
[71]	2×DAVIS (内置IMU) 2×RGB相机 (内置IMU) 16线激光雷达	6DOF	室内室外室内到室外	四轴飞行器摩托车汽车手持	是	2018
[72]	2×DAVIS (内置IMU) RGB-D相机	3DOF	室内	3×地面机器人	是	2018
[73]	DAVIS	6DOF	室内	手持	是	2019
[51]	DAVIS, IMU	6DOF	室内, 仿真	手持	是	2019

下载: 导出CSV

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