Monocular Visual Odometry Using Template Matching and IMU

Zeng, Qingxi; Ou, Bangjun; Lv, Chade; Scherer, Sebastian; Kan, Yuchao

doi:10.1109/jsen.2021.3078847

Cited by 10 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, extensive research has been conducted to develop various positioning systems and techniques for mobile robots [2] and self-driving cars [3] using sensors [4]- [6] such as wheel odometry, ultrasonic odometry, GNSS, inertial navigation system (INS), and visual odometry (VO). It is also crucial to note that these methods are not restrictive, as odometry methods can be multi-modal, i.e., sensor fusion of different sensors can be combined in a single algorithm [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Body-Centered Dynamically-Tuned Error-State Extended Kalman Filter for Visual Inertial Odometry in GNSS-Denied Environments

Abdelaziz,

Elghamrawy,

Noureldin

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The use of visual odometry in autonomous land vehicle and unmanned ground vehicle (UGV) applications has shown to be promising in providing robust and accurate positioning and navigation in GNSS-denied environments. While GNSS is the most accurate and reliable positioning source, it cannot be received indoors. Furthermore, low-cost micro-electro-mechanical system (MEMS)-based inertial sensors are used for positioning but can result in rapid drift during prolonged GNSS outages. To address these limitations, this paper proposes an innovative method of integrating MEMS-based inertial sensors with a vision-based navigation system. The vision-based navigation relies on a visual odometry pipeline modified to replace the feature-matching step with Lucas-Kanade optical flow. While the inertial-based system is derived from the three-dimensional reduced inertial sensor system (3D-RISS) and modified for a bodycentered update. The fusion of both systems is based on an Extended Kalman filter (EKF) to estimate the position and heading information and correct for inertial sensor errors. To assess the effectiveness of the proposed method, real indoor and road test trajectories were conducted using both a Husky A200 UGV and a sensor-equipped minivan. The results show that the body-centered inertial system, aided with reliable vision updates, provides a reliable positioning solution for an extended duration in GNSS-denied environments.

show abstract