Efficient Globally-Optimal Correspondence-Less Visual Odometry for Planar Ground Vehicles

doi:10.1109/ICRA40945.2020.9196595

	Efficient Globally-Optimal Correspondence-Less Visual Odometry for Planar Ground Vehicles
	Ling Gao; Junyan Su; Jiadi Cui; Xiangchen Zeng; Xin Peng; Laurent Kneip
	2020-05-01
会议录名称	PROCEEDINGS - IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION
ISSN	1050-4729
页码	2696-2702
发表状态	已发表
DOI	10.1109/ICRA40945.2020.9196595
摘要	The motion of planar ground vehicles is often non-holonomic, and as a result may be modelled by the 2 DoF Ackermann steering model. We analyse the feasibility of estimating such motion with a downward facing camera that exerts fronto-parallel motion with respect to the ground plane. This turns the motion estimation into a simple image registration problem in which we only have to identify a 2-parameter planar homography. However, one difficulty that arises from this setup is that ground-plane features are indistinctive and thus hard to match between successive views. We encountered this difficulty by introducing the first globally-optimal, correspondence-less solution to plane-based Ackermann motion estimation. The solution relies on the branch-and-bound optimisation technique. Through the low-dimensional parametrisation, a derivation of tight bounds, and an efficient implementation, we demonstrate how this technique is eventually amenable to accurate real-time motion estimation. We prove its property of global optimality and analyse the impact of assuming a locally constant centre of rotation. Our results on real data finally demonstrate a significant advantage over the more traditional, correspondence-based hypothesise-and-test schemes. © 2020 IEEE.
会议录编者/会议主办者	IEEE
关键词	Intelligent robots Branch and bound method Computer vision Ground vehicles Ackermann steering Efficient implementation Global optimality Optimisation techniques Planar homography Real time motion estimation Registration problems Visual odometry
会议名称	2020 IEEE International Conference on Robotics and Automation, ICRA 2020
出版地	NEW YORK
会议地点	Paris, France
会议日期	May 31, 2020 - August 31, 2020
URL	查看原文
收录类别	EI ; CPCI-S
语种	英语
WOS研究方向	Automation & Control Systems ; Engineering ; Robotics
WOS类目	Automation & Control Systems ; Engineering, Electrical & Electronic ; Robotics
WOS记录号	WOS:000712319502002
出版者	Institute of Electrical and Electronics Engineers Inc., United States
EI入藏号	20204309375278
EI主题词	Motion estimation
EISSN	2577-087X
EI分类号	723.5 Computer Applications ; 731.6 Robot Applications ; 741.2 Vision ; 921.5 Optimization Techniques
原始文献类型	Conference article (CA)
来源库	IEEE
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/251839
专题	信息科学与技术学院信息科学与技术学院_PI研究组_Laurent Kneip组信息科学与技术学院_本科生信息科学与技术学院_博士生
作者单位	School of School of Information Science and Technology, ShanghaiTech University
第一作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Ling Gao,Junyan Su,Jiadi Cui,et al. Efficient Globally-Optimal Correspondence-Less Visual Odometry for Planar Ground Vehicles[C]//IEEE. NEW YORK:Institute of Electrical and Electronics Engineers Inc., United States,2020:2696-2702.