Minimalist Visual Inertial Odometry

TL;DR

This paper introduces a minimalist visual-inertial odometry system using only four photodiodes and an IMU, achieving accurate motion estimation with reduced resource requirements.

Contribution

The work demonstrates that a small set of optical measurements combined with an IMU can provide robust planar odometry, optimizing sensor design and decoding speed from minimal signals.

Findings

System accurately tracks ground truth across diverse terrains.

Minimalist sensor setup reduces resource consumption.

Joint optimization of mask parameters and TCN improves speed decoding.

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.