Paper Info
Title
A Binocular Msckf-Based Visual Inertial Odometry System Using Lk Optical Flow
Abstract
The odometry is an important part of intelligent mobile robots to achieve positioning and navigation functions. At present, the mainstream visual odometry locates only through the visual information obtained by camera sensors. Therefore, in the case of insufficient light, texture missing and camera jitter, the visual odometry is difficult to locate accurately. To solve the problem, we propose a binocular MSCKF-based visual inertial odometry system using Lucas-Kanade (LK) optical flow. Firstly, the Inertial Measurement Unit (IMU) is introduced to overcome the above problems. Moreover, LK optical flow algorithm is utilized to process the visual information obtained by the binocular camera, and MSCKF algorithm is employed to realize the fusion of visual information and inertial information, which improves the accuracy and efficiency of the visual inertial odometry system positioning. Finally, the proposed method is simulated on the European Robotics Challenge (EuRoc) dataset by Robot Operating System (ROS), and compared with two other advanced visual inertial odometry systems, ROVIO and MSCKF-mono. A large number of simulations verify that the proposed method can achieve accurate pose estimation, which is superior to the two existing advanced visual inertial odometry systems.
Year
DOI
Venue
2020
10.1007/s10846-020-01222-z
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
Keywords
DocType
Volume
Multi-state constraint Kalman filter (MSCKF), Visual inertial Odometry (VIO), Pose estimation, Robot operating system (ROS)
Journal
100
Issue
ISSN
Citations 
3-4
0921-0296
1
PageRank 
References 
Authors
0.37
0
3
Name
Order
Citations
PageRank
Guangqiang Li110.71
Lei Yu2777.87
Shu-Min Fei3115096.93