LiDAR Enhanced Structure-from-Motion

TL;DR

This paper introduces a LiDAR-enhanced SfM pipeline that combines LiDAR and stereo camera data to improve robustness and accuracy in challenging scenarios, especially with limited image overlap.

Contribution

It presents a novel joint processing method of LiDAR and stereo images to enhance SfM robustness and accuracy in large-scale environments.

Findings

LiDAR integration reduces false matches.

Improves model consistency in large environments.

Outperforms state-of-the-art SfM algorithms in tests.

Abstract

Although Structure-from-Motion (SfM) as a maturing technique has been widely used in many applications, state-of-the-art SfM algorithms are still not robust enough in certain situations. For example, images for inspection purposes are often taken in close distance to obtain detailed textures, which will result in less overlap between images and thus decrease the accuracy of estimated motion. In this paper, we propose a LiDAR-enhanced SfM pipeline that jointly processes data from a rotating LiDAR and a stereo camera pair to estimate sensor motions. We show that incorporating LiDAR helps to effectively reject falsely matched images and significantly improve the model consistency in large-scale environments. Experiments are conducted in different environments to test the performance of the proposed pipeline and comparison results with the state-of-the-art SfM algorithms are reported.