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arXiv · 2605.19990

Minimalist Visual Inertial Odometry

Francesco Pasti·Jeremy Klotz·Nicola Bellotto·Shree K. Nayar
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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, showing that just four visual sensors (pixels) and an IMU 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 the four photodiode measurements. Pairing these estimates with an IMU's angular speed 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 trajectories without any real-world fine-tuning. Our work shows that minimalist sensing enables efficient and accurate planar odometry.

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Francesco Pasti, Jeremy Klotz, Nicola Bellotto, Shree K. Nayar. 2026-05-19. Minimalist Visual Inertial Odometry. https://arxiv.org/abs/2605.19990

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