## 6 Real-World Application
### Experimental Setup  
We use Flir Pointgrey measurement cameras (Richmond, BC, Canada) and Ouster LiDARs (San Francisco, CA, USA)  
for the reconstruction areas. We use a Dahua IP camera for the localization. The sensor resolutions are 1616 \(\times\) 1240 \(\times\) 3 RGB for the Flir cameras running at 10 fps and QHD for the IP camera. The LiDARs were also run at 10 fps. Figure 13: This diagram demonstrates the perception system that our algorithm enables. The strategically deployed _reconstruction area_ is equipped by dense LiDAR and camera coverage. This is responsible for the creation of the _Semantic Mesh Model_ which can be used throughout the rest of the building to localize the vehicle accurately using cameras alone. We intrinsically calibrate the cameras using [PERSON]'s method as implemented in OpenCV. We calibrate every sensor extrinsically to the UTM coordinate system using a highly accurate GPS sensor. We measure the UTM coordinates of specific cracks and markings on the road, and we select corresponding pixels and points manually from the point cloud and the images. We use a simple PnP solver (OpenCV) to calibrate the cameras extrinsically and an RMSE minimization to calibrate the LiDARs initially; then, we refine this estimate using ICP. Both take place in CloudCompare. The algorithm is run on a high-performance gaming PC with an Intel processor (14900K) and Nvidia GPU (RTX4090) (Santa Clara, CA, US). Ubuntu and RTMaps are used for sensor synchronization. As the vehicles are driving slowly, the fact that the RTSP stream from the IP camera has a slight delay and the Flir cameras are not hardware synchronized does not influence the results. For a LiDAR-based detector, we use the classical detector in [45]. Evaluation  
We use five cars for the experiment. They each conduct an initial drive through the reconstruction area, where their _Semantic Mesh Model_ is constructed with the LiDAR-assisted pipeline. The reconstruction process takes 10-30 s depending on the length of the trajectory. Thereafter, they conduct several parking maneuvers, during which the pose estimation is calculated solely based on the IP camera. We compare the detected poses from BAAM and MM-VSM to the LiDAR detector. The results of the experiment are consistent with earlier results (see Figure 15), showing scale and shift improvements over the camera-only method. Concerning the pose estimation, BAAM's error increases significantly, as this is a  
Figure 14: This figure shows our real-world test of the cooperative perception experiment in a satellite image of the BME grounds. The reconstruction area is shown in red. The Localization Area is shown in green. very different camera and very different viewpoint from its training set. This underlines our method's advantage and the fact that BAAM would need retraining before use on a setup like this. Below, in Figure 16, we show an example image of the pose estimation. The green pose estimate is made using the IP camera only. It fits the reference point cloud perfectly. Quantitatively, the errors are calculated similarly to the ZalaZONE measurements. On this dataset, our LiDAR detector [45] actually performs worse than the presented method, as there is no LiDAR coverage from every angle in the pose estimation area. In a reproduced experiment, we recommend to construct a path for reconstruction in which  
Figure 16: Example pose estimation made by the IP camera using the LiDAR-assisted _Semantic Mesh Model_. The black bounding box is generated by our LiDAR detector [45]. The _Semantic Mesh Model_ fits the LiDAR points better in this frame. Outlined in red are the sensor stations. On the top left, the image from the IP camera is plotted alongside the re-projected semantic vertices in red. Figure 15: This figure shows one of the reconstructed vehicles. The LiDAR-assisted model fits the point cloud better. the LiDAR cloud covers every angle of the vehicles in order to use bounding box fitting detectors for the reference shape construction.