A Method of Generating Measurable Panoramic Image for Indoor Mobile Measurement System

TL;DR

This paper presents a novel method for generating high-quality, measurable panoramic images with depth information by fusing LiDAR and image data and employing advanced image stitching techniques for indoor mobile measurement systems.

Contribution

It introduces a self-adaptive framework for dense depth map generation and an optimized image stitching process to produce accurate panoramic images with depth for indoor measurement.

Findings

Successful fusion of LiDAR and image data for depth mapping

Effective image stitching with minimal geometric and photometric artifacts

High-quality panoramic images with measurable depth obtained from real data

Abstract

This paper designs a technique route to generate high-quality panoramic image with depth information, which involves two critical research hotspots: fusion of LiDAR and image data and image stitching. For the fusion of 3D points and image data, since a sparse depth map can be firstly generated by projecting LiDAR point onto the RGB image plane based on our reliable calibrated and synchronized sensors, we adopt a parameter self-adaptive framework to produce 2D dense depth map. For image stitching, optimal seamline for the overlapping area is searched using a graph-cuts-based method to alleviate the geometric influence and image blending based on the pyramid multi-band is utilized to eliminate the photometric effects near the stitching line. Since each pixel is associated with a depth value, we design this depth value as a radius in the spherical projection which can further project the panoramic image to the world coordinate and consequently produces a high-quality measurable panoramic image. The purposed method is tested on the data from our data collection platform and presents a satisfactory application prospects.