Lisondra, M;
Kim, J;
Shimoda, GT;
Zareinia, K;
Saeedi, S;
(2025)
TCB-VIO: Tightly-Coupled Focal-Plane Binary-Enhanced Visual Inertial Odometry.
IEEE Robotics and Automation Letters
, 10
(12)
pp. 12341-12348.
10.1109/LRA.2025.3619774.
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Abstract
Vision algorithms can be executed directly on the image sensor when implemented on the next-generation sensors known as focal-plane sensor-processor arrays (FPSP)s, where every pixel has a processor. FPSPs greatly improve latency, reducing the problems associated with the bottleneck of data transfer from a vision sensor to a processor. FPSPs accelerate vision-based algorithms such as visual-inertial odometry (VIO). However, VIO frameworks suffer from spatial drift due to the vision-based pose estimation, whilst temporal drift arises from the inertial measurements. FPSPs circumvent the spatial drift by operating at a high frame rate to match the high-frequency output of the inertial measurements. In this paper, we present TCB-VIO, a tightly-coupled 6 degrees-of-freedom VIO by a Multi-State Constraint Kalman Filter (MSCKF), operating at a high frame-rate of 250 FPS and from IMU measurements obtained at 400 Hz. TCB-VIO outperforms state-of-the-art methods: ROVIO, VINS-Mono, and ORB-SLAM3.
| Type: | Article |
|---|---|
| Title: | TCB-VIO: Tightly-Coupled Focal-Plane Binary-Enhanced Visual Inertial Odometry |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/LRA.2025.3619774 |
| Publisher version: | https://doi.org/10.1109/lra.2025.3619774 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10216408 |
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