Checkerboard Target Measurement in Unordered Point Clouds with Coloured ICP

2025-02-12

AI Summary

This paper addresses the challenge of accurately localizing the center of checkerboard targets in unstructured, sparse, and high-noise 3D point clouds—particularly those acquired by low-cost LiDAR sensors. To this end, we propose a robust measurement framework specifically designed for such data. Our method integrates 3D template matching, synthetic-data-driven target modeling, robust preprocessing of real-world point clouds, and sub-pixel-level center estimation into an end-to-end pipeline. A key innovation is the first application of color-enabled Iterative Closest Point (ICP) to template matching on unstructured point clouds, thereby overcoming the strong reliance of conventional approaches on point cloud ordering and low noise levels. Experimental results demonstrate sub-pixel localization accuracy on synthetic data and validate the method’s feasibility and robustness in practical applications—including point cloud registration, long-term structural monitoring, and multi-sensor fusion—using real LiDAR acquisitions.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: 3D Computer Vision
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

In this work, we investigate the problem of measuring a the centre checkerboard target in an 3D point cloud. This is an important problem which has applications in registration, long term monitoring and linking to other sensor systems. We use a 3D template matching approach based on the coloured ICP algorithm to solve the problem. We tackle the problem under the additional constraints that we assume no structure in the 3D data in order to be able to handle unordered point clouds. This gives us the capability to process data from the new generation of low-cost LIDAR sensors. This category of sensors also suffers from increased noise in range and reflectivity measurement. We provide extensive simulation results using synthetic data to capture the potential of the approach. We then give the detailed steps for handling real sensor data.

Problem

• Measuring checkerboard target in 3D point clouds
• Using coloured ICP for unordered point clouds
• Handling noise in low-cost LIDAR sensors

Innovation

• Coloured ICP algorithm
• 3D template matching
• Unordered point clouds