Rail Area Extraction Using Laser Point Cloud Data
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Solution Overview
Problem
Current rail area extraction methods in rail transit, which rely on direct detection by cameras and LiDAR, face limitations such as poor anti-interference ability due to lighting and weather conditions, and a relatively short detection distance due to performance limitations of LiDAR.
Innovation Solution
A rail area extraction method based on laser point cloud data that uses side walls parallel to the rail as reference objects, detecting these objects to indirectly extract the rail area, thereby increasing the detection distance beyond the limitations of existing LiDAR technology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR is used to directly detect the rail, then the detection precision is improved, but the detection distance is limited due to performance constraints
Solution Approach 1:
The patent introduces side walls as intermediary objects to indirectly detect the rail area. Instead of directly detecting the rail with LiDAR, the system detects the side walls which are positioned alongside the rails, and then uses the spatial relationship between side walls and rails to determine the rail area. This intermediary approach extends the effective detection distance beyond the direct LiDAR detection limit.
Solution Approach 2:
The patent shifts from direct one-dimensional rail detection to two-dimensional area extraction by detecting side walls on both sides. By detecting objects in the lateral dimension (side walls) and projecting their positions to the horizontal plane, the system extracts the rail area as a two-dimensional region, thereby extending the detection capability beyond the direct line-of-sight limit of the LiDAR.
2Length of stationary object
If camera is used for rail detection, then the detection distance is extended, but the anti-interference ability deteriorates due to lighting and weather conditions
Solution Approach 1:
The patent replaces the optical-based camera detection system with a laser-based LiDAR detection system. While LiDAR has shorter direct detection distance, it is not affected by lighting and weather conditions that degrade camera performance. By substituting the detection mechanism from optical to laser, the system achieves reliable detection in various environmental conditions.
Solution Approach 2:
The patent uses side walls as intermediary objects that are detectable by LiDAR at extended distances. The side walls serve as reliable reference objects that can be detected indirectly, allowing the system to overcome both the camera's vulnerability to environmental interference and the LiDAR's direct detection distance limit.
3Measurement precision
If direct rail detection method is used, then the extraction accuracy is improved, but the detection distance is shortened due to LiDAR performance limitations
Solution Approach 1:
The patent transitions from one-dimensional rail detection to two-dimensional area extraction by detecting side walls on both sides of the rail area. By projecting the detected side wall positions to the horizontal plane and calculating the enclosed area, the system achieves accurate rail area extraction while extending the detection distance through the use of lateral reference objects.
Solution Approach 2:
The patent performs preliminary detection of side walls before extracting the rail area. By first detecting the side walls and establishing their positions as reference objects, the system prepares the necessary spatial information in advance, which is then used to accurately determine the rail area boundaries through projection and calculation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method effectively increases the detection distance of the rail area, improving obstacle detection accuracy and distance, even under the performance limitations of existing LiDAR technology.
Implementation Method 1
a camera and a light detection and ranging (LiDAR) to directly detect a rail
Implementation Method 2
collecting laser point cloud data in front of a train
Data Source
AI summary
A rail area extraction method based on laser point cloud data is provided, including: preprocessing collected laser point cloud data; screening and clustering the laser point cloud data based on a fixed distance segmentation method, and representing laser point cloud data of reference objects by a main laser point cloud data cluster; projecting laser point cloud data of reference objects to a horizontal plane, fitting reference curves based on an improved differential evolution algorithm with a train left side reference curve as an upper boundary and a train right side reference curve as a lower boundary; selecting a target boundary line from upper and lower boundaries based on a laser point cloud data amount-density two-step decision method; calculating a rail area center line based on the target boundary line; and selecting a rail area boundary line extension method or rail area center line extension method to calculate the rail area.