Neural Radiance Field Camera Alignment for Vehicle Coordinate Matching
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Solution Overview
Problem
Existing camera-to-vehicle alignment systems face challenges due to differing coordinate systems between forward-looking cameras and vehicles, leading to offset object detection, which affects applications like redundant lane sensing and pedestrian detection.
Innovation Solution
A system utilizing a neural radiance field technique to generate a rendered portion of the environment based on multiple images, estimate a view direction, and update alignment parameters to align the camera coordinate system with the vehicle system, incorporating a navigation system, camera, and computer to achieve precise alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a forward-looking camera is attached high on the front end of a vehicle in a fixed relationship, then the camera can continuously monitor the environment for viewing, lane sensing, and pedestrian detection, but the camera's coordinate system differs from the vehicle's coordinate system causing offset object detection
Solution Approach 1:
The patent replaces traditional mechanical calibration methods with a neural radiance field-based computational approach. Instead of physically adjusting the camera or using complex mechanical alignment systems, the invention uses neural networks to model the environment and compute alignment parameters, thereby resolving the coordinate system mismatch through software-based spatial transformation.
Solution Approach 2:
The patent dynamically adjusts alignment parameters (such as rotation and translation values) based on neural radiance field rendering comparisons. By changing these parameters iteratively and evaluating the match between rendered and actual images, the system optimizes the coordinate system alignment between camera and vehicle reference frames, improving object detection accuracy.
2Measurement precision
If traditional camera alignment methods are used, then the system structure remains simple, but the alignment accuracy is insufficient leading to offset object detection
Solution Approach 1:
The patent introduces a neural radiance field as an intermediary between the camera images and the vehicle coordinate system. This intermediate representation allows the system to compare rendered views with actual camera images and iteratively optimize alignment parameters, achieving high precision without requiring direct complex mechanical alignment mechanisms.
Solution Approach 2:
The patent creates a virtual copy of the environment through neural radiance field rendering. By generating synthesized images from this virtual model and comparing them with actual camera images, the system can determine alignment parameters without physically moving or adjusting the camera, thereby achieving high accuracy while maintaining relatively simple hardware configuration.
Data Source
AI summary
A system includes a platform operational to move through an environment, a navigation system operational to measure platform poses of the platform in the environment, a camera operational to generate images of the environment at multiple timestamps, and a computer. The computer is operational to receive a first image from the camera, receive a second image from the camera, estimate a view direction of the first camera pose based on a first and second platform poses and alignment parameters, generate a rendered portion of the environment with a neural radiance field technique based on the second image and the alignment parameters, generate a predicted image of the environment as observed along the view direction through the rendered portion of the environment, determine differences between the first image and the predicted image, and update one or more of the plurality of alignment parameters based on the one or more differences.