Calibrating Lidar and Video Subsystems for 3D Trajectory
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Solution Overview
Problem
Accurately calibrating video and lidar subsystems to obtain precise three-dimensional images of a target is challenging without proper registration, as existing methods fail to ensure correspondence between 3D coordinates from lidar and pixel positions in 2D video images.
Innovation Solution
The system combines lidar and video measurements to resolve a six degrees of freedom trajectory, calibrating the lidar subsystem in two stages: first, to provide 3D lidar coordinates, and second, to relate these coordinates with video images, using techniques such as beam steering and coordinate transformations to align data from both systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lidar and video subsystems are integrated without calibration, then the system can capture both 3D and 2D data, but the correspondence between 3D coordinates and pixel positions cannot be established
Solution Approach 1:
The patent applies preliminary action by performing calibration before the actual measurement and imaging operations. The calibration process establishes the correspondence between lidar 3D coordinates and video pixel positions in advance, creating a transformation matrix that can be applied during subsequent target imaging operations without adding complexity to the main measurement process.
Solution Approach 2:
The patent uses a calibration target as an intermediary object that contains known geometric features. This intermediary allows the system to establish the relationship between lidar and video coordinate systems by measuring the known positions of calibration features in both 3D space and 2D image space, thereby enabling accurate correspondence without direct complex alignment between the two subsystems.
2Manufacturing precision
If calibration is performed to ensure accurate correspondence, then 3D image accuracy improves, but the calibration process adds time and complexity
Solution Approach 1:
The patent applies self-service by designing a calibration target that contains encoded calibration information (such as pattern recognition features or known geometric configurations) that allows the system to automatically perform calibration without manual intervention. The target essentially calibrates itself by providing built-in reference features that the imaging system can detect and use to compute the coordinate transformation.
Solution Approach 2:
The patent utilizes parameter changes by transforming the calibration target into different positions and orientations during the calibration process. By changing the spatial parameters (position, angle, distance) of the calibration target relative to the imaging system, the system can robustly determine the transformation parameters between coordinate systems through multiple measurements at different configurations.
3Measurement precision
If a two-stage calibration process is used, then calibration accuracy improves, but the process complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the calibration process into two distinct stages: first, calibrating the lidar subsystem to establish accurate 3D coordinates; second, relating the 3D lidar coordinates to 2D video pixel positions. This segmentation allows each stage to be optimized independently and simplifies the overall implementation by breaking down the complex calibration into manageable steps with specific objectives and methods.
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 enables the generation of accurate, motion-stabilized three-dimensional images by ensuring precise alignment and correspondence between lidar and video data, enhancing the accuracy and reliability of the imaging system.
Implementation Method 1
a three-dimensional lidar (laser radar) subsystem
Implementation Method 2
laser radar subsystem... measurements of the 3D coordinates obtained from the lidar subsystem
Implementation Method 3
a two-dimensional video camera subsystem... pixel positions in the 2D video image
Data Source
AI summary
A system uses range and Doppler velocity measurements from a lidar system and images from a video system to estimate a six degree-of-freedom trajectory of a target. The system calibrates the lidar subsystem with the video subsystem in two stages. In a first stage, the system calibrates the lidar subsystem so that lidar measurements provide 3D lidar coordinates. In a second stage, the system relates the 3D lidar coordinates with a video image obtained from the video subsystem.