Roadside Camera Calibration Using Range Sensor Object Matching
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Solution Overview
Problem
Current calibration methodologies for roadside cameras in ADAS applications are time-consuming, cumbersome, and inefficient, requiring manual surveying to determine real-world positions.
Innovation Solution
A system utilizing roadside cameras and range sensors fixedly mounted to infrastructure, with overlapping fields of view, employs programmatic control logic to filter and constrain data based on regions, movements, and objects of interest, applying a correction factor to calibrate the cameras accurately and precisely.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual surveying and calibration procedures are used, then calibration accuracy can be achieved, but calibration time and complexity increase significantly
Solution Approach 1:
The patent replaces manual mechanical surveying procedures with an automated system using range sensors (LIDAR, radar, ultrasonic) and image processing algorithms. The controller automatically captures range sensor data and image data, processes them through filtering and matching algorithms, and computes calibration parameters without manual intervention, thereby reducing calibration time while maintaining accuracy.
Solution Approach 2:
The calibration system performs self-calibration by automatically capturing data from range sensors and cameras, processing the data through embedded filtering and matching algorithms, and computing calibration parameters autonomously. The system uses its own sensors and processors to calibrate itself without requiring external manual surveying equipment or personnel.
2Measurement precision
If manual surveying and calibration procedures are used, then calibration accuracy can be achieved, but system complexity and operational difficulty increase
Solution Approach 1:
The patent combines multiple functions into an integrated calibration system: range sensors (LIDAR, radar, ultrasonic) are merged with image processing capabilities, and both are controlled by a single controller that performs data capture, filtering, object matching, and calibration parameter computation. This integration reduces operational complexity compared to separate manual surveying and calibration procedures.
Solution Approach 2:
The controller acts as an intermediary that automatically manages the complex calibration process. It receives data from range sensors and cameras, applies filtering algorithms, performs object matching between sensor data and image data, and computes calibration parameters. This intermediary automation eliminates the need for operators to manually coordinate multiple calibration activities.
3Productivity
If automated range sensor-based calibration is implemented, then calibration time and complexity are reduced, but measurement precision may be affected
Solution Approach 1:
The patent applies multiple filtering stages (ROI filtering, MOI filtering, OOI filtering, POI filtering) to progressively refine the data before calibration. This partial processing approach ensures that only relevant and high-quality data points are used for calibration, maintaining precision while automating the process. The excessive filtering also eliminates noise and irrelevant data that could compromise accuracy.
Data Source
AI summary
A system for range sensor-based calibration of roadside cameras includes one or more cameras and range sensors with overlapping fields of view of a roadway. Control logic stored within and executed by a controller includes control logic for capturing image and range sensor data, for filtering the data to focus on first and second regions of interest (ROIs); for filtering the data to focus on first and second movements of interest (MOIs); for filtering the data to focus on first and second objects of interest (OOIs); for filtering the data to focus on first and second positions of interest (POIs); and for defining that objects detected by the camera and sensor and that satisfy both the first and second ROI, MOI, OOI, and POI filters are matching objects. The control logic calibrates the camera by applying a correction factor based on the matching objects.