NanoBench: A Multi-Task Benchmark Dataset for Nano-Quadrotor System Identification, Control, and State Estimation
2026-03-10 • Robotics
Robotics
AI summaryⓘ
The authors created NanoBench, a detailed dataset for tiny drones weighing just 27 grams, called nano-quadrotors. Unlike bigger drones, these tiny ones have unique challenges that make usual models and controllers less accurate. NanoBench collects lots of flight data, including sensor readings, motor commands, and controller information, all carefully synchronized for precision. The authors also provide standard tasks and baseline tests to help researchers improve drone control, estimation, and modeling specifically for these very small flying robots.
nano-quadrotorDC motor nonlinearitieslow-Reynolds number aerodynamicsIMU dataextended Kalman filterPID controllersystem identificationclosed-loop controlstate estimationVicon motion capture
Authors
Syed Izzat Ullah, Jose Baca
Abstract
Existing aerial-robotics benchmarks target vehicles from hundreds of grams to several kilograms and typically expose only high-level state data. They omit the actuator-level signals required to study nano-scale quadrotors, where low-Reynolds number aerodynamics, coreless DC motor nonlinearities, and severe computational constraints invalidate models and controllers developed for larger vehicles. We introduce NanoBench, an open-source multi-task benchmark collected on the commercially available Crazyflie 2.1 nano-quadrotor (takeoff weight 27 g) in a Vicon motion capture arena. The dataset contains over 170 flight trajectories spanning hover, multi-frequency excitation, standard tracking, and aggressive maneuvers across multiple speed regimes. Each trajectory provides synchronized Vicon ground truth, raw IMU data, onboard extended Kalman filter estimates, PID controller internals, and motor PWM commands at 100 Hz, alongside battery telemetry at 10 Hz, aligned with sub-0.5 ms consistency. NanoBench defines standardized evaluation protocols, train/test splits, and open-source baselines for three tasks: nonlinear system identification, closed-loop controller benchmarking, and onboard state estimation assessment. To our knowledge, it is the first public dataset to jointly provide actuator commands, controller internals, and estimator outputs with millimeter-accurate ground truth on a commercially available nano-scale aerial platform.