LiDAR Camera Spatial Synchronization via PNP Pose Optimization
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Solution Overview
Problem
Conventional methods for synchronizing data from multiple sensors in autonomous vehicles, such as LiDAR and camera systems, are time-consuming and lack accuracy, leading to challenges in environmental perception and target detection.
Innovation Solution
An automated multi-sensor spatial data synchronization system that determines corner and center point coordinates in laser and image data, using these coordinates to calculate spatial synchronization relationships between LiDAR and camera assemblies, thereby improving synchronization precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual calibration method is used, then system complexity is reduced, but calibration time increases and calibration accuracy decreases
Solution Approach 1:
The system performs automatic calibration using its own sensors (camera and LiDAR) to detect calibration board features and compute spatial relationships without external intervention. The calibration process is self-contained, requiring no manual operation while achieving high precision through automated feature detection and coordinate calculation.
Solution Approach 2:
The patent replaces manual mechanical calibration operations with automated optical and computational methods. Instead of physically adjusting and measuring components by hand, the system uses camera imaging and LiDAR scanning combined with algorithmic processing to automatically determine spatial synchronization relationships.
2Productivity
If manual calibration method is used, then device complexity is reduced, but productivity decreases due to time consumption
Solution Approach 1:
The system performs preliminary detection of calibration board features (corners, edges, patterns) using camera and LiDAR before computing spatial relationships. This preliminary data collection automates the calibration preparation phase, eliminating time-consuming manual measurement and positioning steps while establishing the foundation for rapid coordinate calculation.
3Measurement precision
If manual calibration method is used, then ease of operation is improved, but measurement precision decreases
Solution Approach 1:
The patent introduces a calibration board as an intermediary object with known geometric features (corners, edges, patterns) that mediates between the camera and LiDAR systems. This intermediary provides standardized reference points for both sensors to detect, enabling precise spatial relationship calculation without requiring complex direct sensor-to-sensor calibration procedures.
Data Source
AI summary
A multi-sensor spatial data auto-synchronization system and method is provided. The method may include collecting laser point cloud data through a laser radar and pre-processing the laser point cloud data; collecting image point cloud data through a monocular camera and collecting intrinsic parameters of the camera by using a calibration board; performing plane fitting on the pre-processed laser point cloud data to determine coordinates of fitted laser point cloud data; performing image feature extraction on the image point cloud data; calculating pose transformation matrices for the laser point cloud data coordinates and an image feature data result by using a PNP algorithm; and optimizing a rotation vector and a translation vector in the pose transformation matrix. The present invention achieves automatic calculation of a spatial synchronization relationship between two sensors, and greatly improves the data synchronization precision of the laser radar and the monocular camera.