Learned Inertial Odometry for Autonomous Drone Racing

TL;DR

This paper introduces a novel learning-based inertial odometry system for autonomous drone racing, combining model-based filtering with learned modules to improve pose estimation solely from IMU and thrust data.

Contribution

It presents a new inertial odometry algorithm that outperforms existing methods and is suitable for high-speed drone racing using only inertial and thrust measurements.

Findings

Outperforms state-of-the-art filter-based and visual-inertial odometry methods.

Comparable to visual-inertial odometry using known gate information.

Demonstrates effectiveness in autonomous drone racing scenarios.

Abstract

Inertial odometry is an attractive solution to the problem of state estimation for agile quadrotor flight. It is inexpensive, lightweight, and it is not affected by perceptual degradation. However, only relying on the integration of the inertial measurements for state estimation is infeasible. The errors and time-varying biases present in such measurements cause the accumulation of large drift in the pose estimates. Recently, inertial odometry has made significant progress in estimating the motion of pedestrians. State-of-the-art algorithms rely on learning a motion prior that is typical of humans but cannot be transferred to drones. In this work, we propose a learning-based odometry algorithm that uses an inertial measurement unit (IMU) as the only sensor modality for autonomous drone racing tasks. The core idea of our system is to couple a model-based filter, driven by the inertial measurements, with a learning-based module that has access to the thrust measurements. We show that our inertial odometry algorithm is superior to the state-of-the-art filter-based and optimization-based visual-inertial odometry as well as the state-of-the-art learned-inertial odometry in estimating the pose of an autonomous racing drone. Additionally, we show that our system is comparable to a visual-inertial odometry solution that uses a camera and exploits the known gate location and appearance. We believe that the application in autonomous drone racing paves the way for novel research in inertial odometry for agile quadrotor flight.