Lidar Calibration Target Localization for Long-Range Point Cloud Accuracy
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Solution Overview
Problem
Lidar devices face challenges in accurately calibrating for long distances due to angular misalignments and occlusion, leading to incomplete or inaccurate point clouds, which affects the precision of distance and reflectivity measurements.
Innovation Solution
A method for localizing a lidar calibration target by generating a point cloud based on a presumed location, identifying edges within the cloud, and revising the location based on comparisons between actual and hypothetical edge positions, ensuring accurate alignment and calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of stationary object
If lidar calibration is performed at long distances, then the calibration range is extended, but angular misalignments and occlusion cause incomplete or inaccurate point clouds
Solution Approach 1:
The system performs preliminary localization of the calibration target by generating a point cloud and identifying its position before actual calibration measurements are taken. This preliminary action allows the system to detect and correct for angular misalignments and occlusion issues before they compromise the calibration data quality
Solution Approach 2:
The system generates feedback by comparing the detected calibration target position in the point cloud with the expected position. This feedback loop enables real-time correction of angular misalignments and verification that the target is properly visible, preventing incomplete calibration data
2Measurement precision
If the calibration target position is not precisely localized, then the calibration process is simpler, but distance and reflectivity measurements become inaccurate
Solution Approach 1:
The lidar system performs self-localization of the calibration target by using its own point cloud generation capability to identify and locate the target. This self-service approach eliminates the need for external positioning systems or manual measurement tools, achieving high precision without proportionally increasing system complexity
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 approach enhances the accuracy and completeness of lidar calibrations by precisely localizing the calibration target, improving the precision of distance and reflectivity measurements, especially at long ranges.
Implementation Method 1
The distance between the lidar device and a given object may be determined based on a time of flight of the corresponding light pulses that interact with the given object
Implementation Method 2
At least a portion of the light pulses may be redirected back toward the lidar (e.g., due to reflection or scattering) and detected by a detector subsystem
Data Source
AI summary
Example embodiments relate to methods for localizing light detection and ranging (lidar) calibration targets. An example method includes generating a point cloud of a region based on data from a light detection and ranging (lidar) device. The point cloud may include points representing at least a portion of a calibration target. The method also includes determining a presumed location of the calibration target. Further, the method includes identifying, within the point cloud, a location of a first edge of the calibration target. In addition, the method includes performing a comparison between the identified location of the first edge of the calibration target and a hypothetical location of the first edge of the calibration target within the point cloud if the calibration target were positioned at the presumed location. Still further, the method includes revising the presumed location of the calibration target based on at least the comparison.