Abstract

This paper presents a novel system for autonomous,vision-based drone racing combining learned data abstraction,nonlinear filtering, and time-optimal trajectory planning. Thesystem has successfully been deployed at the first autonomousdrone racing world championship: the2019 AlphaPilot Challenge.Contrary to traditional drone racing systems, which only detectthe next gate, our approach makes use of any visible gate andtakes advantage of multiple, simultaneous gate detections tocompensate for drift in the state estimate and build a global mapof the gates. The global map and drift-compensated state estimateallow the drone to navigate through the race course even whenthe gates are not immediately visible and further enable to plana near time-optimal path through the race course in real timebased on approximate drone dynamics. The proposed system hasbeen demonstrated to successfully guide the drone through tightrace courses reaching speeds up to8 m/sand ranked second atthe2019 AlphaPilot Challenge

Highlights

  • Human pilots have shown that drones are capable of flying through complex environments, such as race courses, at breathtaking speeds

  • Due to the high speeds at which drones must fly in order to beat the best human pilots, the challenging visual environments, and the limited computational power of drones, autonomous drone racing raises fundamental challenges in real-time state estimation, perception, planning, and control

  • Perception Of the two stereo camera pairs available on the drone, only the two central forward-facing cameras are used for gate detection and, in combination with inertial measurement unit (IMU) measurements, to run visual-inertial odometry (VIO)

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Summary

INTRODUCTION

Due to the high speeds at which drones must fly in order to beat the best human pilots, the challenging visual environments (e.g., low light, motion blur), and the limited computational power of drones, autonomous drone racing raises fundamental challenges in real-time state estimation, perception, planning, and control. The drone’s large translational and rotational velocities cause large optic flow, making robust feature detection and tracking over sequential images difficult and often causing substantial drift in the VIO state estimate [9] To overcome this difficulty, several approaches exploiting the structure of drone racing with gates as landmarks have been developed, e.g., [10] and [11], where the drone locates itself relative to gates.

SYSTEM OVERVIEW The system is composed of five functional groups
GATE DETECTION
Stage 1
Stage 2
STATE ESTIMATION
Measurement Modalities
PATH PLANNING
Attitude Control
VIII. RESULTS
Gate Detection
Gate 2
DISCUSSION AND CONCLUSION
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