# Build Your Own Visual-Inertial Drone: A Cost-Effective and Open-Source Autonomous Drone

arXiv: https://arxiv.org/abs/1708.06652 · HTML: https://mlsanity.com/paper/1708.06652/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2017.

## Built on by (most cited first, 1 of 1)

- [Improving Wind Resistance Performance of Cascaded PID Controlled Quadcopters using Residual Reinforcement Learning](https://mlsanity.com/paper/2308.01648/) (2023)

## Abstract (arXiv)

This paper describes an approach to building a cost-effective and research grade visual-inertial odometry aided vertical taking-off and landing (VTOL) platform. We utilize an off-the-shelf visual-inertial sensor, an onboard computer, and a quadrotor platform that are factory-calibrated and mass-produced, thereby sharing similar hardware and sensor specifications (e.g., mass, dimensions, intrinsic and extrinsic of camera-IMU systems, and signal-to-noise ratio). We then perform a system calibration and identification enabling the use of our visual-inertial odometry, multi-sensor fusion, and model predictive control frameworks with the off-the-shelf products. This implies that we can partially avoid tedious parameter tuning procedures for building a full system. The complete system is extensively evaluated both indoors using a motion capture system and outdoors using a laser tracker while performing hover and step responses, and trajectory following tasks in the presence of external wind disturbances. We achieve root-mean-square (RMS) pose errors between a reference and actual trajectories of 0.036m, while performing hover. We also conduct relatively long distance flight (~180m) experiments on a farm site and achieve 0.82% drift error of the total distance flight. This paper conveys the insights we acquired about the platform and sensor module and returns to the community as open-source code with tutorial documentation.