Minimalist Visual Inertial Odometry
Summary
A minimalist visual-inertial odometry approach uses four photodiodes with optical Gabor masks and a temporal convolutional network to achieve accurate planar motion estimation for differential-drive robots, validated across diverse indoor and outdoor terrains without real-world fine-tuning.
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Paper page - Minimalist Visual Inertial Odometry
Source: https://huggingface.co/papers/2605.19990
Abstract
A minimalist visual-inertial odometry approach uses four photodiodes with optical Gabor masks and a temporal convolutional network to achieve accurate planar motion estimation for differential-drive robots.
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 fordifferential-drive robots. Our key insight is that four downward-facingphotodiodesthat sense the world throughoptical Gabor masksproduce signals that encode speed. Based on this, we jointly optimize the mask parameters alongside aTemporal Convolutional Network(TCN) using aphysically-grounded simulator. The resulting model decodes speed from just the four measurements produced by thephotodiodes. 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.
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