NDLI: Confidence analysis of feature points for visual-inertial odometry of urban vehicles

Content Provider	IET Digital Library
Author	Lee, Chang Ryeol Yoon, Kuk Jin
Abstract	Visual-inertial odometry (VIO) is the process of estimating ego-motion using a camera and the inertial measurement unit (IMU). It shows outstanding performance in estimating the ego-motion of a vehicle at the absolute scale thanks to the gyroscope and the accelerometer. However, VIO has some difficulties in estimating the translation in large-scale outdoor environments because the feature points along the motion direction and distant feature points in the images can cause degenerate situations. To resolve these difficulties, the authors propose to infer the confidence measures of the feature points and appropriately incorporate them into the Kalman filter-based VIO. The confidence is computed from the motion direction and the displacements of the tracked feature points under authors’ urban canyon prior. It is applied in situations where the camera is moving forward towards the measurement noise covariance of the Kalman filter for ego-motion estimation. Experimental results on the public KITTI dataset show that VIO outperforms monocular and stereo-visual odometries, and the proposed VIO with confidence analysis achieves a translation error of 1.82% and a rotation error of 0.0018 deg/m.
Starting Page	1130
Ending Page	1138
Page Count	9
ISSN	1751956X
Volume Number	13
e-ISSN	17519578
Issue Number	Issue 7, Jul (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/13/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2018.5196
Journal	IET Intelligent Transport Systems
Publisher Date	2019-03-13
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Absolute Scale Thanks Authors Camera Computer Vision And Image Processing Technique Confidence Analysis Confidence Measures Distance Measurement Distant Feature Points Ego-motion Estimation Filtering Method in Signal Processing Gyroscopes Inertial Measurement Unit Inertial Navigation System Kalman Filter Kalman Filter-based VIO Large-scale Outdoor Environments Measurement Noise Covariance Mobile Robots Motion Direction Motion Estimation Nonlinear Filter Optical, Image And Video Signal Processing Robot Vision Statistics Stereo Image Processing Stereo-visual Odometries Tracked Feature Point Urban Vehicle Visual-inertial Odometry
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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