3D Point Cloud Reconstruction and SLAM as an Input
This work addresses real-time outdoor surface reconstruction for robotics applications, but appears incremental as it builds on existing SLAM and reconstruction techniques.
The paper tackles 3D surface reconstruction by comparing traditional and data-driven methods for dense point cloud inputs, and proposes a system using tightly-coupled SLAM with IMU pre-integration and pose graph optimization to generate deskewed point clouds and odometry for real-time outdoor reconstruction.
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.