## 2 Related Works  
ETDE under limited processing power and real-time constraints is critical for modern PIG control systems in natural gas pipelines. Existing methods for ETDE are primarily designed for short pipelines or slower-moving, non-isolated robots, where these constraints are less critical. These approaches can be categorized based on the sensors used: contact sensors [6,7], 2D cameras [8, 9, 10, 11], stereo cameras [12,13], and 3D ranging sensors [14,15]. They are summarized in Table 1.  
The first approach involves attaching contact sensors to the front of the robot to detect elbows and T-junctions by physically interacting with the inner pipe surface. In [6], three contact sensors are evenly placed circumferentially to detect elbows by observing deviations in the center point of the contact points. Consecutive center points are aggregated to estimate the direction and radius of the elbow. Similarly, ref. [7] uses four contact sensors to detect elbows, where the direction and position are computed from the two most deviated sensors. Because of their simplicity in processing, these approaches are ideal for the limited processing power and real-time systems in general. However, they are unsuitable for PIG in natural gas pipelines since they detect features only after partial entry into the elbow, limiting reaction time for the control system. Moreover, at high speeds, contact sensors risk damaging both the pipelines and themselves. The second approach employs a single 2D camera to detect the elbow and T-junction. In [8], a camera paired with an LED illuminator is used to detect elbows and T-junctions by analyzing high-reflection areas caused by small incident angles at the elbows and T-junctions. The location and shape of these areas in the image are directly inputted to a fuzzy logic control algorithm. To improve the accuracy regarding lighting variations, a deep learning technique based on convolutional neural networks is introduced in [9] to detect elbows and T-junctions from 2D camera images. In [10], a rotating line-shaped laser paired with a 2D camera generates consecutive images to form a full scan of the pipe. The elbow and T-junction create a gap area in the scanning image. By analyzing the shape and position of this gap area, the elbow and T-junction can be detected, and their direction is also estimated. The approach in [11] uses a camera paired with four circle-and-dot laser pointers to detect the elbow. The circle and dot can only be seen in the image if an elbow exists in front of the robot. The direction and distance to the elbow can be inferred by comparing the distortion pattern of circles and dots to known patterns. These approaches rely on image processing which is of high computational complexity and opposed to the PIG constraints. Additionally, relying on the deformation of a pattern which is abstract information leads to late detection and partial parameter estimation with low granularity. The third approach involves a stereo camera. In [12], the existence of T-junction is detected by edge detection using one camera of the stereo camera while stereo matching between both cameras of the stereo camera is performed to find the distance. However, the detection and distance measurement with the stereo camera is not accurate in the pipeline environment due to unclear features on the pipe wall. To address this issue, the approach in [13] additionally uses the second distance measurement mechanism using a rotating laser profiler. When the robot is close to the T-junction detected by the stereo camera, the highly accurate laser profiler is activated to detect and measure the distance to it. These approaches share the same computational complexity issues with the 2D camera. The fourth approach utilizes 3D ranging sensors. In [14], a pseudo-3D ranging sensor using a rotating 2D LIDAR detects elbows and T-junctions by recognizing outlier points inside or outside the straight pipe.