Robust Visual Inertial Odometry Estimation Based on Adaptive Interactive Multiple Model Algorithm

Wang, Lei; Xia, Shicheng; Xi, Hengliu; Li, Shuangxi; Wang, Le

doi:10.1007/s12555-020-0781-2

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An accurate ego-motion estimation of a vehicle in a global navigation satellite system (GNSS) denied environment has been a challenging task in robotics and autonomous driving for several decades [1]. To tackle this issue, the ego-motion estimation algorithm using camera information, so-called Visual Odometry (VO), has been studied [2], [3], [4]. VO incrementally estimates a relative 6-DOF pose between consecutive images.…”

Section: Introductionmentioning

confidence: 99%

Image Gradient-based Monocular Visual-Inertial Odometry

Kim,

Jung,

Park

2024

Proceedings of International Conference on Artificial Life and Robotics

View full text Add to dashboard Cite

This paper presents image gradient-based monocular visual-inertial odometry (VIO) algorithm, using image gradient measurements, robust to illumination change. We expand the measurements from the reprojected feature locations on the image coordinates to the corresponding image gradients. The iterated EKF and low-pass pyramid are adapted to reduce the linearization error in the multi-state constraint Kalman filter (MSCKF) measurement update process. We verify that our proposed algorithm outperforms both conventional indirect and direct MSCKF-based VIO algorithms by evaluating the pose estimation performance.

show abstract