Customizable Stochastic High-Fidelity Model of the Sensors and Camera Onboard a Fixed Wing Autonomous Aircraft

Area of Science:

• Aerospace Engineering
• Robotics
• Computer Vision

Background:

• Autonomous aircraft navigation relies on accurate sensor data for state estimation.
• Key sensors include accelerometers, gyroscopes, magnetometers, GNSS receivers, air data systems, and cameras.
• Realistic sensor models are crucial for developing and testing navigation algorithms.

Purpose of the Study:

• To present realistic and customizable models for autonomous aircraft navigation sensors.
• To provide an open-source C++ implementation for generating sensor errors and realistic camera images.
• To facilitate the design, development, and testing of inertial, visual, and visual-inertial navigation algorithms.

Main Methods:

• Development of pseudo-random error models for individual sensors based on datasheet specifications.
• Integration of sensor models with a simulated aircraft pose to generate realistic camera images.
• Release of a ready-to-use C++ software package for non-expert users.

Main Results:

• Realistic and customizable sensor models for accelerometers, gyroscopes, magnetometers, GNSS, air data systems, and cameras.
• A functional C++ implementation enabling the generation of time-stamped sensor errors.
• Simulated camera images that accurately represent real-world imagery from a given aircraft pose.

Conclusions:

• The developed sensor models and open-source software provide a valuable tool for advancing autonomous aircraft navigation research.
• The realistic simulation capabilities can accelerate the development and validation of sophisticated navigation algorithms.
• The accessibility of the C++ implementation empowers a broader range of researchers and developers.