Lidar Calibration Using Reference Point Clouds
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Solution Overview
Problem
Conventional lidar device calibration and localization methods rely on ground-truth calibration targets, which can be inconvenient and time-consuming, especially for autonomous vehicles that need to operate far from calibration depots or with partially disassembled components, leading to potential inaccuracies due to mechanical shifts and environmental factors.
Innovation Solution
A method where a well-calibrated and localized lidar device on one vehicle can calibrate and localize another vehicle by capturing point clouds and using pose information, confidence thresholds, and transformation matrices to determine the calibration and localization of the second vehicle, allowing for proxy calibrations without ground-truth targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional lidar calibration methods using ground-truth targets are used, then calibration accuracy is maintained, but calibration time and operational complexity increase significantly
Solution Approach 1:
The patent uses point cloud data from a first lidar device as a virtual copy or reference model to calibrate the second lidar device. Instead of requiring physical ground-truth targets, the system creates a digital representation of the environment from one sensor and uses it to validate and calibrate another sensor, thereby eliminating the need for time-consuming physical calibration targets while maintaining calibration accuracy
Solution Approach 2:
The patent introduces point cloud data as an intermediary between the two lidar devices. The processed point cloud from the first device serves as a mediating reference that enables calibration of the second device without direct physical interaction or ground-truth targets, reducing calibration time while preserving accuracy through computational comparison
2Reliability
If ground-truth calibration targets are used, then calibration reliability is ensured, but device complexity and operational inconvenience increase
Solution Approach 1:
The system performs self-calibration by using its own lidar data. The first lidar device captures point cloud data that serves as a reference for calibrating the second lidar device, eliminating the need for external ground-truth targets and reducing operational complexity while maintaining reliability through internal data validation
Solution Approach 2:
The point cloud data generated by the lidar system serves multiple functions: it represents the environment for navigation, serves as a calibration reference for sensor alignment, and provides geometric constraints for localization. This multi-functionality eliminates the need for separate ground-truth targets, improving operational convenience while maintaining calibration reliability
3Productivity
If proxy calibration without ground-truth targets is implemented, then operational efficiency improves, but calibration precision may be compromised
Solution Approach 1:
The patent replaces the mechanical calibration target system with a computational approach using point cloud processing. Instead of physical targets and manual measurement, the system uses algorithmic comparison of point cloud data from two lidar devices to determine calibration parameters, improving efficiency while maintaining precision through mathematical optimization
Solution Approach 2:
The patent transitions from two-dimensional target patterns to three-dimensional point cloud data for calibration. By utilizing the full spatial information from lidar scans rather than flat target patterns, the system achieves both higher efficiency and maintained precision through richer geometric constraints in multiple dimensions
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 efficient and accurate calibration and localization of lidar devices in autonomous vehicles, even far from calibration depots, by leveraging the calibration of a nearby vehicle, thus maintaining operational readiness and reducing the need for frequent return trips for calibration.
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 calibration and localization of a light detection and ranging (lidar) device using a previously calibrated and localized lidar device. An example embodiment includes a method. The method includes receiving, by a computing device associated with a second vehicle, a first point cloud captured by a first lidar device of a first vehicle. The first point cloud includes points representing the second vehicle. The method also includes receiving, by the computing device, pose information indicative of a pose of the first vehicle. In addition, the method includes capturing, using a second lidar device of the second vehicle, a second point cloud. Further, the method includes receiving, by the computing device, a third point cloud representing the first vehicle. Yet further, the method includes calibrating and localizing, by the computing device, the second lidar device.