Vehicle Sensor Alignment Using 2D-to-3D Scene Registration
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Solution Overview
Problem
Conventional sensor alignment and calibration in autonomous vehicles is time-consuming and inefficient, particularly when dealing with sensors capturing data in different dimensionalities.
Innovation Solution
A method and system that utilize sensor data captured during vehicle operation to determine a rigid transformation and scaling factor between a camera and LiDAR, enabling efficient alignment and calibration by constructing 3D representations from 2D image and 3D point cloud data, allowing for simultaneous data fusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional sensor alignment and calibration methods are used, then sensors can be aligned and calibrated, but the process is time-consuming and inefficient
Solution Approach 1:
The system uses sensor data captured during normal vehicle operation to perform alignment and calibration automatically, without requiring separate calibration procedures or additional time. The calibration process serves itself by utilizing existing operational data from multiple sensors (camera, LiDAR, radar) to determine transformation parameters and scaling factors, thereby eliminating dedicated calibration time while maintaining accuracy
Solution Approach 2:
The system pre-processes sensor data during vehicle operation to construct 3D representations and point cloud models in advance. By continuously building and updating these spatial models using data from multiple sensor modalities, the system prepares alignment information beforehand, enabling rapid calibration when needed without time-consuming processing during actual alignment operations
2Adaptability or versatility
If sensors capturing data in different dimensionalities are aligned, then multi-sensor data fusion can be achieved, but the alignment process becomes more complex and time-intensive
Solution Approach 1:
The system transforms 2D camera image data into 3D spatial representations by constructing 3D models from 2D images and matching them with 3D point cloud data from LiDAR and radar. This dimensionality transformation approach unifies different sensor data types into a common 3D coordinate system, simplifying the alignment process while enabling comprehensive multi-sensor fusion without proportionally increasing complexity
Solution Approach 2:
The system determines transformation parameters (rotation, translation) and scaling factors by comparing features across different sensor modalities. By automatically adjusting these parameters based on matched features between 3D representations and point clouds, the system adapts to dimensional differences between sensors without requiring complex manual calibration procedures for each sensor type
Data Source
AI summary
Described herein are systems, methods, and non-transitory computer readable media for performing an alignment between a first vehicle sensor and a second vehicle sensor. Two-dimensional (2D) data indicative of a scene within an environment being traversed by a vehicle is captured by the first vehicle sensor such as a camera or a collection of multiple cameras within a sensor assembly. A three-dimensional (3D) representation of the scene is constructed using the 2D data. 3D point cloud data also indicative of the scene is captured by the second vehicle sensor, which may be a LiDAR. A 3D point cloud representation of the scene is constructed based on the 3D point cloud data. A rigid transformation is determined between the 3D representation of the scene and the 3D point cloud representation of the scene and the alignment between the sensors is performed based at least in part on the determined rigid transformation.