LiDAR Pose Calibration for Ground Truth Dataset Generation
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Solution Overview
Problem
Current autonomous vehicle datasets are not well generalized across different environments, making it costly and challenging for research groups to process, analyze, and maintain suitable platforms for development and validation, especially due to high costs associated with calibration and data collection.
Innovation Solution
A method and system for generating a ground truth dataset through sensor calibration and time synchronization, involving LiDAR pose calculation, data alignment, and projection of 3D points onto images, using a supervised method for intrinsic parameter calibration and GNSS-IMU for inertial navigation, and synchronization of data acquisition among sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor calibration and data collection procedures are performed regularly to ensure data quality, then measurement precision and reliability are improved, but cost and time consumption increase
Solution Approach 1:
The patent performs sensor calibration and data collection procedures in advance to create a ground truth dataset before actual autonomous vehicle operation. By pre-calibrating sensors and pre-collecting reference data in controlled environments, the system eliminates the need for frequent calibration and data collection during operational phases, thereby reducing time consumption while maintaining measurement precision.
2Reliability
If multiple sensors are calibrated and synchronized to generate ground truth datasets, then reliability and measurement precision are improved, but device complexity and cost increase
Solution Approach 1:
The patent introduces a central processing system that acts as an intermediary to coordinate calibration and synchronization of multiple sensors including LiDAR, cameras, and GPS. This intermediary system manages the complex interactions between sensors, performs unified calibration, and generates the ground truth dataset, thereby improving reliability while managing system complexity through centralized control rather than distributed complexity.
3Productivity
If existing autonomous vehicle datasets are used without recalibration, then cost and time are reduced, but adaptability and generalization to different environments deteriorate
Solution Approach 1:
The patent performs sensor calibration and data collection in multiple different environments with varying parameters such as lighting conditions, weather, and geographic locations. By changing environmental parameters during the ground truth dataset creation process, the system ensures that the resulting dataset is adaptable and generalizable to different operating conditions, thereby improving environmental versatility while maintaining processing efficiency through standardized calibration procedures.
Data Source
AI summary
A method of generating a ground truth dataset for motion planning is disclosed. The method includes performing data alignment, collecting data in an environment, using sensors, calculating, among other sensors, light detecting and ranging (LiDAR)'s poses, stitching multiple LiDAR scans to form a local map, refining positions in the local map based on a matching algorithm, and projecting 3D points in the local map onto corresponding images.