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Automatic Registration of TLS-TLS and TLS-MLS Point Clouds Using a Genetic Algorithm.

Li Yan¹, Junxiang Tan², Hua Liu³

¹School of Geodesy and Geomatics, Wuhan University, Luoyu Road 129, Wuhan 430079, China. lyan@sgg.whu.edu.cn.

Sensors (Basel, Switzerland)

|August 30, 2017

Summary

This study introduces an efficient genetic algorithm (GA) for accurate point cloud registration in Light Detection and Ranging (LiDAR) remote sensing. The method achieves high precision for terrestrial (TLS) and mobile (MLS) LiDAR data, significantly reducing processing time.

Keywords:

genetic algorithm mobile LiDAR scanning point cloud registration terrestrial LiDAR scanning

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Area of Science:

Geomatics Engineering
Remote Sensing
Computer Vision

Background:

Accurate point cloud registration is crucial for integrating data from multiple Light Detection and Ranging (LiDAR) scans.
Existing methods often require manual intervention or struggle with diverse scanning platforms like terrestrial (TLS) and mobile (MLS) LiDAR.

Purpose of the Study:

To develop an efficient and automatic point cloud registration method using a genetic algorithm (GA).
To improve the accuracy and reduce the computational cost of aligning TLS-TLS and TLS-MLS point clouds.

Main Methods:

A novel registration approach based on genetic algorithm (GA) is proposed.
Constraints from built-in GPS and sensor orientation are used to optimize the GA search space.
A new fitness function, Normalized Sum of Matching Scores, is introduced for precise evaluation.
The method integrates GA with Iterative Closest Point (ICP) to accelerate optimization.

Main Results:

The proposed GA method achieved root-mean-square errors (RMSE) of 3-5 mm for TLS-TLS registration.
RMSE for TLS-MLS registration was 2-4 cm, demonstrating high accuracy across different platforms.
Integrating GA with ICP reduced optimization time by approximately 50%.

Conclusions:

The genetic algorithm-based method provides an efficient and accurate solution for point cloud registration in LiDAR remote sensing.
The approach effectively aligns data from both terrestrial and mobile scanning platforms.
Hybridizing GA with ICP offers a significant speed-up in processing time without compromising accuracy.