# Study on Elevator Recognition Techniques for Upper-Limb-Disabled Wheelchair Users
## 2 Materials and Methods
### LiDAR-Sensor-Based Gate Structure Shape Recognition Method
#### 2.2.3 Elevator Feature Point-Based Recognition Method  
In order to recognize the elevator, detection conditions should be set according to the characteristics of the elevator structure. This study presents a recognition algorithm using the characteristics of line segments generated through the IEPF line segment extraction algorithm. The elevator recognition technique determines whether or not the recognition technique is implemented by comparing the number of lines generated through the IEPF with the minimum number of lines that can represent the shape of the elevator, as shown in Figure 5. The elevator recognition technique first performs low-reflective elevator recognition, and if it is not detected, high-reflective elevator recognition is performed to determine whether the elevator is detected. The point-cloud type containing elevator information is divided into two cases, depending on the location of the LiDAR sensor being scanned, as shown in Figure 6. CASE 1 of Figure 6 shows the case where the scan was performed at a position where the LiDAR sensor could detect the entire structure of the elevator, and CASE 2 shows the result of the scan at a position where the right side of the elevator was not detected. Therefore, in this study, the elevator feature detection conditions are examined based on the extracted line segments to recognize the two types of elevators determined by the location of the LiDAR sensor. Figure 5: Elevator detection algorithm flowchart. Figure 6: Elevator feature detection. The test of the elevator feature detection conditions is based on the angle between the adjacent line segments, and the length of the line segment with the elevator door frame and door information; the algorithm for this is shown in Algorithm 1. The final condition of the elevator recognition algorithm process is based on the distance between the test points corresponding to the elevator door frame. In this study, an elevator recognition algorithm is carried out based on the minimum effective door width of 0.8 m for disabled elevators under the Korea Act on Guaranteeing Convenience Promotion of Elderly Pregnant Women with Disabilities for the elevator feature test [28]. ```
Notation \(s_{n}=\) start point of \(n^{th}\) line \(e_{n}=\) end point of \(n^{th}\) line \(\theta_{n}=\) degree between adjacent line \(\theta_{thr}=\) threshold degree range \(d_{n}=n^{th}\) line length \(d_{fthr}=\) elevator frame threshold range \(d_{dthr}=\) elevator door threshold range \(l_{s}=\) straight line through points \(e_{n},\ s_{n+1}\) flag \(=\) elevator detection status \(\text{Detect }(s_{n},\ e_{n},\ d_{n},\ \theta_{n})\) {  for i from 1 to 4  if\(\theta_{n}\) is within the threshold degree: \(n=n+1\) if i is 1 or 3: if\(d_{n}\) is within the \(d_{fthr}\) else return endif if i is 4: \(\text{coordinates }=(e_{n},\ s_{n+4})\) flag \(=\) true break endif if\(d_{n}\) is within the \(d_{dthr}\): else break endif else if i is 3: if an angle between \(l_{n+2},\ l_{s}\) is within \(\theta_{thr}\) if an angle between \(l_{n+3},\ l_{s}\) is within \(\theta_{thr}\) coordinates \(=(e_{n},\ s_{n+3})\) flag \(=\) true endfor if flag is true if coordinates distance satisfy elevator width return coordinates endif
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**Algorithm 1**Elevator Detection  
Due to the nature of the LiDAR sensors, the point-cloud, which includes information on high-reflective elevators, is generated in a very irregular form due to high surface reflectance, making it difficult to use points that scan the elevator structures. According to [PERSON] [29; 30], disturbance observer-based control (DOBC) is a control methodology that estimates and compensates for disturbances in linear and nonlinear systems and has shown good performance in the presence of disturbances. However, this technique is suitable for smooth nonlinear disturbances and is not suitable for non-smooth noisy environments. Therefore, in this study, a recognition technique using the adjacent wall of the elevator is performed as a means of recognizing the high-reflective elevator.