{"ID":2860679,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.03919","arxiv_id":"2510.03919","title":"TCB-VIO: Tightly-Coupled Focal-Plane Binary-Enhanced Visual Inertial Odometry","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.","short_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 senso...","url_abs":"https://arxiv.org/abs/2510.03919","url_pdf":"https://arxiv.org/pdf/2510.03919v1","authors":"[\"Matthew Lisondra\",\"Junseo Kim\",\"Glenn Takashi Shimoda\",\"Kourosh Zareinia\",\"Sajad Saeedi\"]","published":"2025-10-04T19:47:52Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}