Robust and Autonomous Stereo Visual-Inertial Navigation for Non-Holonomic Mobile Robots

Unlike micro aerial vehicles, most mobile robots have non-holonomic constraints, which makes lateral movement impossible. Consequently, the vision-based navigation systems that perform accurate visual feature initialization by moving the camera to the side to ensure a sufficient parallax of the imag...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on vehicular technology 2020-09, Vol.69 (9), p.9613-9623
Main Authors: Chae, Hee-Won, Choi, Ji-Hoon, Song, Jae-Bok
Format: Article
Language:English
Subjects:
Algorithms
Autonomous navigation
Cameras
Coders
Dead reckoning
Feature extraction
Image degradation
Inertial navigation
Inertial sensing devices
keyframes
Micro air vehicles (MAV)
Mobile robots
Navigation
Navigation systems
Parallax
Robot vision systems
Robots
Robustness
visual-inertial systems
wheeled mobile robots
Wheels
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Unlike micro aerial vehicles, most mobile robots have non-holonomic constraints, which makes lateral movement impossible. Consequently, the vision-based navigation systems that perform accurate visual feature initialization by moving the camera to the side to ensure a sufficient parallax of the image are degraded when applied to mobile robots. Generally, to overcome this difficulty, a motion model based on wheel encoders mounted on a mobile robot is used to predict the pose of a robot, but it is difficult to cope with errors caused by wheel slip or inaccurate wheel calibration. In this study, we propose a robust autonomous navigation system that uses only a stereo inertial sensor and does not rely on wheel-based dead reckoning. The observation model of the line feature modified with vanishing-points is applied to the visual-inertial odometry along with the point features so that a mobile robot can perform robust pose estimation during autonomous navigation. The proposed algorithm, i.e., keyframe-based autonomous visual-inertial navigation (KAVIN) supports the entire navigation system and can run onboard without an additional graphics processing unit. A series of experiments in a real environment indicated that the KAVIN system provides robust pose estimation without wheel encoders and prevents the accumulation of drift error during autonomous driving.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2020.3004163