| Paper ID | MMSP-1.1 |
| Paper Title |
ATVIO: ATTENTION GUIDED VISUAL-INERTIAL ODOMETRY |
| Authors |
Li Liu, Ge Li, Peking University Shenzhen Graduate School, China; Thomas H Li, Peking University, China |
| Session | MMSP-1: Multimedia Signal Processing |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Multimedia Signal Processing: Multimedia Environments |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Visual-inertial odometry (VIO) aims to predict trajectory by egomotion estimation. In recent years, end-to-end VIO has made great progress. However, how to handle visual and inertial measurements and make full use of the complementarity of cameras and inertial sensors remains a challenge. In the paper, we propose a novel attention guided deep framework for visual-inertial odometry (ATVIO) to improve the performance of VIO. Specifically, we extraordinarily concentrate on the effective utilization of the Inertial Measurement Unit (IMU) information. Therefore, we carefully design a one-dimension inertial feature encoder for IMU data processing. The network can extract inertial features quickly and effectively. Meanwhile, we should prevent the inconsistency problem when fusing inertial and visual features. Hence, we explore a novel cross-domain channel attention block to combine the extracted features in a more adaptive manner. Extensive experiments demonstrate that our method achieves competitive performance against state-of-the-art VIO methods. |
