A Simple and Efficient Registration of 3D Point Cloud and Image Data for Indoor Mobile Mapping System

TL;DR

This paper presents a simple, efficient method for registering 3D LiDAR point clouds with optical images to improve alignment accuracy in indoor mobile mapping systems, using feature extraction and cost map optimization.

Contribution

It introduces a novel registration approach combining feature extraction from LiDAR and images with cost map-guided optimization for better alignment accuracy.

Findings

Achieves improved registration accuracy in experiments.

Demonstrates robustness through cost map and loss function integration.

Provides a computationally efficient solution for indoor mapping applications.

Abstract

Registration of 3D LiDAR point clouds with optical images is critical in the combination of multi-source data. Geometric misalignment originally exists in the pose data between LiDAR point clouds and optical images. To improve the accuracy of the initial pose and the applicability of the integration of 3D points and image data, we develop a simple but efficient registration method. We firstly extract point features from LiDAR point clouds and images: point features is extracted from single-frame LiDAR and point features from images using classical Canny method. Cost map is subsequently built based on Canny image edge detection. The optimization direction is guided by the cost map where low cost represents the the desired direction, and loss function is also considered to improve the robustness of the the purposed method. Experiments show pleasant results.