# Connecting the Elderly Using VR: A Novel Art-Driven Methodology
## 3 Materials and Methods
### Methods and Tools
#### 3.4.2 Interiors Sensing for Three-Dimensional Reconstruction
This part of the implementation procedure included (i) the selection and identification of chessboard patterns for camera calibration targets with reflective tape for camera-LIDAR calibration, (ii) multi-camera calibration based on bundle adjustment optimization, (iii) custom algorithms for camera-LIDAR calibration, (iv) a custom reflective target with an Apriltag pattern, designed and detected on the image space, (v) detecting the center of the reflective target in the LIDAR space, (vi) selecting static frames based on target movement thresholds, (vii) developing a robust Perspective-n-Point algorithm to estimate the displacements and orientation between cameras and the LIDAR sensor, (viii) nonlinear optimization to obtain the final calibration parameters, and (ix) a transformation tree--\"tf-tree\"--which was estimated for use in the ROS-compatible 3D reconstruction module. The data collection included 20 individual scans of \(\sim\)3 mm resolution with the Faro Focus ([[https://www.faro.com/en/Products/Hardware/Focus-Laser-Scanners](https://www.faro.com/en/Products/Hardware/Focus-Laser-Scanners)]([https://www.faro.com/en/Products/Hardware/Focus-Laser-Scanners](https://www.faro.com/en/Products/Hardware/Focus-Laser-Scanners)), accessed on 28 February 2024) 3D terrestrial laser scanner. A survey was carried out in the living room with the first beta version of the mobile mapping platform using two sensors:  
1. The Intel RealSense T265 Tracking Camera ([[https://www.intelrealsense.com/tracking-camera-t265/](https://www.intelrealsense.com/tracking-camera-t265/)]([https://www.intelrealsense.com/tracking-camera-t265/](https://www.intelrealsense.com/tracking-camera-t265/)), accessed on 28 February 2024) for relative positioning. 2. The Microsoft(r) Kinect v.2 Depth Camera ([[https://learn.microsoft.com/en-us/azure/kinect-dk/depth-camera](https://learn.microsoft.com/en-us/azure/kinect-dk/depth-camera)]([https://learn.microsoft.com/en-us/azure/kinect-dk/depth-camera](https://learn.microsoft.com/en-us/azure/kinect-dk/depth-camera)), accessed on 28 February 2024) to acquire depth images and point clouds. A photographic survey of small objects and selected furniture was also performed to provide visual data for 3D reconstruction by means of multi-view and single-image approaches. A quality assessment of all acquired data was performed in the field, after each photo capture, 3D scanning, and mobile mapping mission. Finally, the reconstructed 3D models were exploited to manually generate parametric/semantically rich 3D CAD (Rhino) models from photogrammetric mesh models (i.e., Figure 5). The results of the tasks supporting this objective were the 3D models supporting our VR environment. 3D models of specific pieces of furniture were also created from the data collection with the mobile mapping platform. Multiple depth maps captured with a Microsoft Kinect  
Figure 4: The outline of the geometrical calibration algorithm. depth sensor were converted to point clouds. These were then registered and combined into a single 3D mesh model. The triangular mesh was then textured from RGB images taken with a separate digital camera.