| title | Minimalist Visual Inertial Odometry |
|---|---|
| authors | Francesco Pasti, Jeremy Klotz, Nicola Bellotto, Shree K. Nayar |
| date | 2026-05-19 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.19990 |
| affiliation | Columbia University |
| upvotes | 1 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Visual-Inertial Odometry(VIO), which is critical to mobile robot navigation, uses cameras with a large number of pixels. Capturing and processing camera images requires significant resources. This work presents a minimalist approach to planar odometry, demonstrating that just four visual measurements and an IMU can provide robust motion estimation for differential-drive robots. Our key insight is that four downward-facing photodiodes that sense the world through optical Gabor masks produce signals that encode speed. Based on this, we jointly optimize the mask parameters alongside a Temporal Convolutional Network (TCN) using a physically-grounded simulator. The resulting model decodes speed from just the four measurements produced by the photodiodes. Pairing these estimates with the angular speed from an IMU yields a continuous planar trajectory. We validate our approach with a prototype sensor mounted on a differential drive robot. Across diverse indoor and outdoor terrains, our system closely tracks the reference ground truth without any real-world fine-tuning. Our work shows that minimalist sensing enables efficient and accurate planar odometry. |
cite
@misc{pasti2026minimalist,
title={Minimalist Visual Inertial Odometry},
author={Francesco Pasti and Jeremy Klotz and Nicola Bellotto and Shree K. Nayar},
year={2026},
eprint={2605.19990},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.19990}
}