Multi-LiDAR Calibration with Navigation Noise Compensation
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Solution Overview
Problem
Existing calibration methods for lidar and integrated navigation in autonomous driving are limited by specific types of lidar and environmental requirements, and do not adequately account for noise in navigation data, leading to inaccurate extrinsic parameter estimation.
Innovation Solution
A method for multi-lidar and integrated navigation calibration that includes obtaining initial extrinsic parameters, adjusting for vehicle vibration and lidar clock differences, and optimizing poses to compensate for vertical components, using a two-stage approach to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If geometric-feature extraction method is used for calibration, then calibration can be performed, but the method is limited to specific types of lidar and environmental requirements
Solution Approach 1:
The patent applies universality by developing a calibration method that works with multiple lidar types (rotating mechanical lidar, solid-state lidar, multi-lidar systems) and various environments (structured artificial environment, unstructured natural environment) without requiring specific geometric features or environmental conditions. The method uses point cloud data from any lidar configuration and can handle different scanning patterns, making it universally applicable while maintaining high measurement precision through a unified optimization framework.
2Measurement precision
If existing calibration methods are used, then calibration can be performed, but noise in navigation data is not taken into consideration
Solution Approach 1:
The patent implements feedback by incorporating the optimization results back into the calibration process. The method iteratively optimizes extrinsic parameters by considering both point cloud data and navigation data, using the feedback from the optimization to refine the extrinsic parameter estimates. This feedback mechanism allows the system to account for noise in navigation data and continuously improve the accuracy of extrinsic parameter estimation, making the calibration more robust to noisy conditions.
3Productivity
If on-line multiple lidar calibration techniques are applied, then calibration can be performed, but limited requirements for environment and lidar types impede wide application
Solution Approach 1:
The patent applies segmentation by dividing the calibration process into distinct modules: point cloud data processing, navigation data processing, extrinsic parameter optimization, and compensation matrix calculation. Each module can independently handle different lidar types and environmental conditions, making the overall system more adaptable. The segmentation allows the system to process data from various lidar configurations separately and then integrate the results, enabling efficient on-line calibration across diverse environments and hardware setups.
Data Source
AI summary
The present invention discloses a method and system of calibration for multi-lidar and integrated-navigation, comprising: obtaining raw point cloud data from multiple lidars and integrated navigation data, and calculating an initial extrinsic parameter characterizing a conversion relationship between each lidar and said integrated navigation data; adjusting the initial extrinsic parameter and the integrated navigation data to obtain a first-type extrinsic parameter between each lidar and the integrated navigation apparatus, as well as optimized integrated navigation data; obtaining, according to the first-type extrinsic parameter, a compensation matrix for compensating a vertical component of an extrinsic parameter between each lidar and the integrated navigation apparatus; and obtaining a second-type extrinsic parameter between individual lidars according to the compensation matrix and the first-type extrinsic parameter of each lidar as well as the optimized integrated navigation data.