3D Point Cloud Reconstruction and SLAM as an Input

TL;DR

This paper compares traditional and data-driven surface reconstruction methods using dense point clouds and introduces a real-time outdoor reconstruction system integrating SLAM, IMU pre-integration, and surface reconstruction techniques.

Contribution

It presents a novel system combining tightly-coupled SLAM with surface reconstruction, enhancing accuracy and enabling real-time outdoor surface reconstruction.

Findings

Data-driven methods outperform traditional ones in reconstruction quality.

The integrated system achieves real-time outdoor surface reconstruction.

IMU pre-integration improves SLAM accuracy.

Abstract

To handle the different types of surface reconstruction tasks, we have replicated as well as modified a few of reconstruction methods and have made comparisons between the traditional method and data-driven method for reconstruction the surface of an object with dense point cloud as input. On top of that, we proposed a system using tightly-coupled SLAM as an input to generate deskewed point cloud and odometry and a Truncated Signed Distance Function based Surface Reconstruction Library. To get higher accuracy, IMU(Inertial Measurement Unit) pre-integration and pose graph optimization are conduct in the SLAM part. With the help of the Robot Operating System, we could build a system containing those two parts, which can conduct a real-time outdoor surface reconstruction.