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Related Experiment Video

Updated: Aug 2, 2025

Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management

Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management

Published on: September 12, 2017

Real-Time Safe Landing Zone Identification Based on Airborne LiDAR.

Ali Massoud^1,2, Ahmed Fahmy¹, Umar Iqbal³

¹Department of Electrical and Computer Engineering, Queen's University, Kingston, ON K7L 3N6, Canada.

Sensors (Basel, Switzerland)

|April 13, 2023

Summary

This summary is machine-generated.

This study introduces an efficient real-time algorithm for identifying safe landing zones using LiDAR data. The method assists aircraft in challenging environments, enhancing pilot safety and navigation.

Keywords:

LiDAR LiDAR point cloud airborne laser scanning digital surface model real-time LiDAR data processing slope map

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Area of Science:

Robotics and Automation
Aerospace Engineering
Geospatial Science

Background:

Real-time Digital Surface Model (DSM) generation is crucial for aircraft landing in degraded visual environments.
Existing airborne laser scanning (ALS) filtering algorithms are computationally expensive and slow, hindering real-time applications.
Safe landing zone (SLZ) identification is vital to prevent accidents caused by poor visibility and obstacles.

Purpose of the Study:

To design and implement an efficient real-time algorithm for SLZ identification on limited-resource embedded processors.
To process 3D LiDAR point cloud data for safe aircraft/helicopter landings.
To overcome the limitations of computationally expensive and time-consuming existing algorithms.

Main Methods:

Developed a novel, efficient algorithm for real-time processing of 3D LiDAR point cloud data.
Implemented the algorithm in C++ for deployment on embedded systems.
Validated the algorithm's performance using professional flight simulation software.

Main Results:

The algorithm successfully identified the best safe landing zone (SLZ) in real-time.
Demonstrated the algorithm's effectiveness in assisting pilots during simulated landings.
Validated the accuracy of the developed method by comparing results with existing maps.

Conclusions:

The developed algorithm provides an efficient, real-time solution for SLZ identification using LiDAR data.
This technology can significantly enhance aircraft safety, especially in degraded visual environments.
The method is suitable for deployment on embedded processors, enabling real-time assistance for pilots.