Nonlinear System Identification Nano-drone Benchmark

TL;DR

This paper presents a comprehensive benchmark dataset and evaluation framework for nonlinear system identification using real-world data from a nano-quadrotor, facilitating fair comparison and advancing research in agile aerial robotics.

Contribution

It introduces a large-scale, real-world dataset and multi-horizon prediction metrics for system identification of a nano-quadrotor, along with baseline models and open-source resources.

Findings

Baseline models highlight the difficulty of accurate prediction with real-world noise.

The dataset enables evaluation of multi-step prediction errors.

Open-source tools support reproducibility and method comparison.

Abstract

We introduce a benchmark for system identification based on 75k real-world samples from the Crazyflie 2.1 Brushless nano-quadrotor, a sub-50g aerial vehicle widely adopted in robotics research. The platform presents a challenging testbed due to its multi-input, multi-output nature, open-loop instability, and nonlinear dynamics under agile maneuvers. The dataset comprises four aggressive trajectories with synchronized 4-dimensional motor inputs and 13-dimensional output measurements. To enable fair comparison of identification methods, the benchmark includes a suite of multi-horizon prediction metrics for evaluating both one-step and multi-step error propagation. In addition to the data, we provide a detailed description of the platform and experimental setup, as well as baseline models highlighting the challenge of accurate prediction under real-world noise and actuation nonlinearities. All data, scripts, and reference implementations are released as open-source at https://github.com/idsia-robotics/nanodrone-sysid-benchmark to facilitate transparent comparison of algorithms and support research on agile, miniaturized aerial robotics.