Checkerboard Target Measurement in Unordered Point Clouds with Coloured ICP

TL;DR

This paper presents a novel method for accurately measuring checkerboard targets in unordered 3D point clouds using coloured ICP, enabling applications in sensor registration and long-term monitoring, especially with noisy, low-cost LIDAR data.

Contribution

The paper introduces a 3D template matching approach based on coloured ICP that works on unordered, noisy point clouds without requiring structural assumptions.

Findings

Effective in synthetic simulations demonstrating robustness.

Capable of processing real sensor data with detailed methodology.

Handles unordered, noisy point clouds from low-cost LIDAR sensors.

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.