LiDAR Maximum Range Determination via Point Density Regression
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Solution Overview
Problem
Current methods lack a reliable and efficient way to determine the maximum range of a LiDAR sensor, especially in complex environments with masking effects, such as urban areas with buildings, where the sensor's resolution is low and different targets have similar geometries.
Innovation Solution
A method involving the calculation of point densities within a LiDAR point cloud, using a classification algorithm to identify predefined environment objects like the ground, and a regression curve to determine the maximum range based on these densities, allowing for robust range estimation even in adverse conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LiDAR systems are used in complex environments with masking effects (e.g., urban areas with buildings), then the sensor can operate in diverse real-world conditions, but the resolution decreases and different targets have similar geometries making reliable range determination difficult
Solution Approach 1:
The patent introduces an intermediary approach by using the ground surface as a reference mediator. Instead of directly measuring the range to distant objects in complex environments, the system measures the point density of the ground surface and uses this intermediary measurement to infer the LiDAR sensor's maximum range through a regression curve, thereby achieving accurate range determination despite environmental masking effects
Solution Approach 2:
The patent replaces direct geometric measurement methods with a statistical/probabilistic approach. Instead of relying on traditional geometric object detection and edge detection that fails in complex environments, the system substitutes this with analyzing the statistical distribution of point densities on the ground surface and using a regression curve to infer range, effectively replacing mechanical/geometric measurement with statistical inference
2Productivity
If traditional object detection methods based on geometrical characteristics are used, then the system can identify objects, but it fails when resolution is low and different targets have similar geometries
Solution Approach 1:
The patent extracts the essential characteristic of point density from the LiDAR data, separating this statistical property from the geometric shape analysis that fails in complex environments. By focusing solely on the density distribution of points on the ground surface rather than attempting to recognize specific object geometries, the system achieves reliable range determination without being misled by similar geometries of different objects
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
Enables reliable and efficient detection of the maximum range of a LiDAR sensor, independent of the specific environment, improving its performance in various applications, including autonomous driving, by using a regression curve to infer the range from calculated point density quotients.
Implementation Method 1
LiDAR sensors also supply information about the reflection characteristics of the illuminated targets by measuring the intensity of the reflected/backscattered light
Implementation Method 2
Such systems and methods involve a form of three-dimensional laser scanning. In general, the term 'LiDAR' thus encompasses a series of techniques which employ laser light for measuring the distance to a certain target
Data Source
AI summary
A method for determining the maximum range of a LiDAR sensor. The method includes: providing a LiDAR point cloud using a LiDAR sensor, which images an environment of the LiDAR sensor at a certain point in time within a predefined field of view of the LiDAR sensor in a three-dimensional manner;identifying at least two different point sets within the LiDAR point cloud, each imaging an area in the environment that was identified as belonging to a predefined environment object;calculating the particular areas that are imaged by the point sets in each case, and dividing the number of LiDAR points imaging these areas by the individually imaged corresponding areas to obtain at least two different point densities;calculating a quotient from the point densities, and using this quotient to ascertain the maximum range of the LiDAR sensor, for which a previously stored regression curve is used.