TinyCVIO: A Constellation-Aided Visual-Inertial Odometry System for Nanodrones

2026-09-24

AI Summary

This study addresses the challenge of real-time, accurate state estimation for nano-drones under stringent sensing and computational constraints by proposing a lightweight visual-inertial odometry system. Through the co-design of miniature perception, visual processing, and estimation algorithms, the system exploits LED constellations to provide known geometric priors. A novel rigid-board measurement model is introduced to preserve inter-LED geometric constraints, enabling efficient fixed-dimensional filtering without pre-calibrating positions or yaw angles. Implemented on a dual-core microcontroller, the system integrates millimeter-scale camera streaming with front-end tracking and QR boundary estimation techniques. Experimental results demonstrate that trajectory error is reduced by 27% compared to planar-point methods, achieving an average absolute trajectory error of 3.5–3.7 cm, a relative pose error of 0.50%–0.60%, and an end-to-end latency of approximately 16 ms.

Abstract

Nanodrones require accurate, real-time state estimation under severe sensing and computational constraints. We present TinyCVIO, a visual-inertial odometry system that co-designs miniature sensing, visual processing, and estimation for a commodity dual-core microcontroller with 520 kB SRAM. Lightweight LED constellations provide known geometry without surveyed positions or yaw angles, assuming placement on a common level plane. A streaming visual frontend tracks LED observations from a millimeter-scale camera at 29.2 FPS, while a rigid-board measurement model retains inter-LED constraints and streaming QR bounds estimation workspace for a fixed filter-state size. Across 19 hand-held hardware-in-the-loop datasets, the rigid-board model reduces mean absolute trajectory error by 27% relative to planar points. The complete system runs onboard a Crazyflie across nine flights at three speeds, achieving 3.5-3.7 cm mean absolute trajectory error and 0.50-0.60% relative pose error over 10 m segments, with mean estimate latency of 15.7-16.3 ms.

Problem

• Nanodrones
• State Estimation
• Visual-Inertial Odometry
• Resource Constraints
• Real-time

Innovation

• Visual-Inertial Odometry
• Nanodrones
• LED Constellation
• Rigid-board Measurement Model
• Resource-constrained Microcontroller