## 3 Proposed Method
### Data Analysis  
According to Table 1, the 3D LiDAR system uses a light source to measure distances, representing the distance to reflective objects as points. The point data are displayed as a 3D point cloud, with the light source rotating 360 degrees. These point cloud data, which contain specific characteristics of objects, can be used for object detection algorithms. Although data distortion may occur in foggy weather with high humidity, in this study, we applied object detection algorithms that are not affected by the time of day or night or weather conditions when the target objects are within 20 m [19]. However, the collected data capture the entire surrounding environment, including the ground of the aircraft apron, people, cars, and other obstacles, as shown in Figure 3. Therefore, unnecessary information must be filtered. In this study, ground data filtering was performed to specify the regions required for the algorithm. Ground and distorted data filtering were conducted using the 3D RANSAC technique. By adjusting the field of view of the sensor data, could we processed only the regions requiring specific object recognition without processing the entire scanning dataset, ensuring computational speed. Additionally, the spatial position between the jetbridge and aircraft was fixed, and cylindrical objects  
Figure 1: Configuration of the jetbridge autonomous sensor system [18]. were identified as aircraft engines, ensuring a high detection rate compared to other studies due to the specific nature of the data training [20]. In this study, the 3D RANSAC (random sample consensus) algorithm was used to filter ground data during the preprocessing of 3D LiDAR data. The 3D RANSAC method is a robust technique for efficiently estimating models in noisy data. It effectively extracts ground data and removes unnecessary points, enhancing the accuracy of the semantic segmentation process. The basic operation of RANSAC is as follows:  
* Random sampling: Select a random subset of samples from the data set. * Model estimation: Estimate a model based on the selected samples. * Consensus set formation: Identify data points that fit the estimated model to form a consensus set. \begin{table}
\begin{tabular}{l l} \hline Range (80\%, 1024 @ 10 Hz mode) & 170 m @ \textgreater{} 90\% detection probability, 100 klx sunlight \\ \hline Range (10\%, 1024 @ 10 Hz mode) & 90 m @ \textgreater{} 90\% detection probability, 100 klx sunlight \\ \hline Min–Max Range & 0.5 m–150 m \\ \hline Vertical Resolution & 64 channels \\ \hline Horizontal Resolution & 1024 or 2048 (configurable) \\ \hline Rotation Rate & 10 or 20 Hz (configurable) \\ \hline Field of View & Vertical (+22.5\({}^{\circ}\) to –22.5\({}^{\circ}\)), Horizontal 360\({}^{\circ}\) \\ \hline Angular Accuracy & Vertical: \(\pm\)0.01\({}^{\circ}\), Horizontal: \(\pm\)0.01\({}^{\circ}\) \\ \hline Range Resolution & 0.1 cm \\ \hline \end{tabular}
\end{table} 
Table 1: Optical performance of 3D LiDAR systems [19]. Figure 2: Schematics of the jetbridge system used for automation [18]. * Model evaluation: Evaluate the size of the consensus set and select the model that includes the most data points. * Iteration: Repeat the above process for a predefined number of iterations to find the optimal model. Particularly, 3D RANSAC performs these steps in a 3D space to estimate various models such as planes, spheres, and cylinders. However, the shapes must be well-defined, and it can be challenging to distinguish and classify small objects or those with non-standard features. This study utilized 3D RANSAC to extract ground data in the aircraft apron environment. As shown in Figure 3, ground data are collected with noise from 3D LiDAR scan data, and accurately extracting these ground points is crucial. The process of filtering ground data using 3D RANSAC in this study was as follows: [21]. * Initial data collection: Collect raw point cloud data scanned with a 3D LiDAR sensor. There data comprise points from various objects, including the ground.