Visual Landing

Onboard landing guidance independent of ground infrastructure

Dissimilar to existing ILS, GPS or AHRS, enables safe landing for fixed-wing aircraft in Visual Meteorological Conditions (VMC). Identifies airport runways and provides ownship position from approach to touchdown.

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(Development view, not a pilot interface)

Related Content

May 24, 2022

Daedalean Concluded a Joint Research Project with the FAA on Neural Network-Based Runway Landing Guidance for General Aviation

July 15, 2021

Land Safely! Daedalean’s Visual Landing System

July 2, 2021

Visual Landing System flight test, 2021-07-02, 5x speed

June 21, 2024

A Deep Dive into Leonardo's Flight Tests of Daedalean's Visual Awareness System

How We Use AI

We use a type of AI called machine learning to create a neural network—the core component responsible for recognition of objects appearing in the video stream.

Training machine learning models in the lab

Data from real flights
Data from simulated flights
Training
dataset
Testing
dataset
Validation
dataset

AI component within the airborne application

Input
Video stream from cameras
Pre-processing
Preparing frames for analysis (using classical software)
Algorithm frozen after training and testing, not evolving or learning in flight
Convolutional Neural Network
Identifying the boundaries and orientation of the runway
Post-processing
Sending data output and calculating the suggested maneuver for the flight director (using classical software)
Output
Glideslope deviation, lateral deviation, height relative to runway

Let’s connect!

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Visual Traffic Detection
Visual Navigation
Visual Wire Detection
Visual Landing
Visual Vertical Landing
Landing Site Finder
Runway Incursion Detection

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