3D Parallax Estimation for Camera-LiDAR Sensor Placement
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Solution Overview
Problem
Existing methods for parallax estimation between sensors in autonomous vehicles, such as cameras and LiDAR, face challenges in accurately quantifying parallax in three-dimensional environments, leading to difficulties in sensor placement optimization and object detection due to physical limitations and varying vantage points.
Innovation Solution
A method involving the generation of a two-dimensional grid based on the field of view of a camera sensor, determining intersection points and solid angles between camera and LiDAR rays, and extracting metrics from a matrix to adjust sensor arrangements, facilitating reduced parallax and improved data fusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If sensors are placed at different physical locations on the autonomous vehicle, then the field of view and coverage are improved, but parallax error increases making data fusion difficult
Solution Approach 1:
The patent transitions from traditional 2D image-based parallax estimation to 3D spatial reasoning by casting rays in three-dimensional space. The system generates a 3D grid of rays from each sensor's optical center, calculates intersection points in 3D space, and determines solid angles to quantify parallax. This dimensional elevation enables accurate parallax measurement across heterogeneous sensors (camera and LiDAR) positioned at different locations, resolving the contradiction between expanded field of view and reduced parallax error.
2Adaptability or versatility
If heterogeneous sensors (camera and LiDAR) are used, then sensing capabilities are enhanced, but parallax quantification becomes more difficult due to different detection mechanisms
Solution Approach 1:
The patent introduces an intermediary geometric framework that bridges different sensor types. By representing both camera and LiDAR detections as rays originating from their respective optical centers and using 3D intersection points as common reference markers, the system creates a unified geometric language. This intermediary approach allows parallax to be quantified consistently across heterogeneous sensors through solid angle calculations, enabling data fusion while maintaining enhanced sensing capabilities.
3Device complexity
If traditional 2D image-based techniques are used for parallax estimation, then the methodology is simple, but accuracy in three-dimensional environments is insufficient
Solution Approach 1:
The patent elevates parallax estimation from 2D image space to 3D physical space. Instead of measuring parallax solely in image coordinates, the system casts rays in three-dimensional space, calculates 3D intersection points, and computes solid angles that capture the true spatial separation between sensor vantage points. This dimensional transformation significantly improves measurement precision in three-dimensional environments while maintaining computational tractability through systematic grid generation and metric extraction.
Data Source
AI summary
Methods, systems, and products for parallax estimation for sensors for autonomous vehicles may include generating a two-dimensional grid based on a field of view of a first sensor. For each respective point in the grid, a three-dimensional position of an intersection point between a first ray from the first sensor and a second ray from a second sensor may be determined. For each respective intersection point, a respective solid angle may be determined based on a first three-dimensional vector from the first sensor and a second three-dimensional vector from the second sensor to the intersection point. A matrix may be generated based on a distance from the first sensor, a distance from the second sensor, and the solid angle for each respective intersection point. At least one metric may be extracted from the matrix. An arrangement of the first and second sensors may be adjusted based on the metric(s).