Lidar Calibration Target Localization Using Edge-Based Point Clouds
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Solution Overview
Problem
Lidar devices face challenges in accurately calibrating for long distances due to angular misalignments and occlusion of calibration targets, leading to incomplete or inaccurate point clouds, which affects the precision of distance and reflectivity measurements.
Innovation Solution
A method is introduced to localize a lidar calibration target by generating a point cloud based on a presumed location, identifying edges within the cloud, and revising the location by comparing actual and hypothetical edge positions, allowing for precise alignment and calibration of the lidar device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a presumed location is used for calibration target positioning, then calibration process can be initiated, but localization accuracy is insufficient leading to measurement errors
Solution Approach 1:
The system generates feedback by comparing the actual point cloud data with the expected point cloud at the presumed location. The edge location difference serves as feedback to iteratively update and refine the presumed location until convergence, thereby improving both calibration reliability and location precision simultaneously
Solution Approach 2:
The method performs preliminary localization by generating an initial presumed location before actual calibration. This preliminary action allows the system to prepare calibration data structures and expected point clouds in advance, enabling faster and more reliable calibration execution
2Length of stationary object
If lidar calibration is performed at long distances, then measurement range is extended, but angular misalignment and occlusion increase causing incomplete point clouds
Solution Approach 1:
The system dynamically adjusts the calibration process by iteratively refining the target location based on actual point cloud feedback. This dynamic adaptation allows the system to compensate for angular misalignment and occlusion effects that become more significant at long distances, maintaining point cloud completeness and calibration reliability
Solution Approach 2:
The method transitions from working with raw 3D point cloud data to extracting 2D edge projections for comparison. This dimensional transformation simplifies the localization problem and enables more robust edge-based matching that is less sensitive to distance-related issues like angular misalignment and occlusion
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 of lidar calibrations by ensuring precise localization of the calibration target, improving the reliability of distance and reflectivity measurements, even at long ranges and in scenarios where the target may be occluded or misaligned.
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.