LiDAR Ground Line Detection Using Predictive Height Ranges
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Solution Overview
Problem
Existing LiDAR systems face challenges in accurately detecting ground lines due to signal attenuation and noise, leading to fuzzy and unreliable ground line detection, especially at farther distances.
Innovation Solution
A LiDAR detection method that compares point cloud data from closer ground lines to predict the height range of more distant ground lines, selecting reliable ground points to improve the clarity and stability of the detected ground lines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of stationary object
If LiDAR detects ground lines at farther distances, then the detection range is extended, but the signal attenuation and noise increase leading to fuzzy and unreliable ground line detection
Solution Approach 1:
The method performs preliminary detection of ground lines at closer distances (i-1 and i-2) before detecting the distant ground line (i). By using the point cloud data from these closer, clearer ground lines to predict the height range of the distant ground line, the system prepares reference information in advance that helps maintain detection precision even at extended ranges.
Solution Approach 2:
The point cloud data from closer ground lines (i-1 and i-2) serves as an intermediary to facilitate the detection of distant ground lines. These intermediate measurements act as a bridge, providing height range predictions that guide the selection of reliable ground points for distant ground line detection, thereby maintaining clarity despite signal attenuation.
2Area of stationary object
If LiDAR uses all scanning points to generate ground lines, then the detection coverage is maximized, but the reliability of ground points decreases due to noise and signal attenuation
Solution Approach 1:
The method applies different quality standards to different scanning points based on their reliability. By using point cloud data from closer ground lines to predict height ranges, the system can identify and select only the reliable ground points that fall within these predicted ranges, while excluding noisy or unreliable points. This local quality filtering maintains ground point reliability across the entire detection coverage area.
3Ease of operation
If LiDAR detects ground lines sequentially from near to far, then the processing order is simplified, but the clarity of distant ground lines deteriorates due to cumulative signal attenuation
Solution Approach 1:
The method implements feedback by using the detection results from closer ground lines (i-1 and i-2) to inform and improve the detection of distant ground lines (i). The height range predictions derived from nearby ground lines are fed back into the detection process for distant lines, allowing the system to maintain clarity despite sequential processing and cumulative signal attenuation.
Data Source
AI summary
Embodiments of this application disclose a method, device, storage medium, and LiDAR for LiDAR detection. The method includes: obtaining point cloud data corresponding to an i−1th ground line and point cloud data corresponding to an i−2th ground line; calculating a predicted height range of the ith ground line based on the point cloud data corresponding to the i−1th ground line and the point cloud data corresponding to the i−2th ground line; detecting a scanning points on the ith row of the scanning line based on the predicted height range to determine a target point in the scanning points; and obtaining the ith ground line based on the target point.