2D-3D Sensor Calibration Using Color-Depth Overlap
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Solution Overview
Problem
Calibrating sensors in mobile devices, such as autonomous vehicles, to ensure accuracy and consistency in perceiving a field of view is challenging, especially when the sensors become offset during operation, affecting the device's ability to identify and track objects for autonomous navigation.
Innovation Solution
A method involving the use of a 2D camera and a 3D depth sensor to capture images and depth maps, isolating background and foreground colors on a calibration surface, and optimizing the calibration transform between the sensors based on the overlap of these colors to align and calibrate the sensors in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are calibrated during operation to maintain accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system performs self-calibration by automatically detecting the calibration surface in the environment and computing correction transforms without external intervention. The mobile device uses its own sensors to identify the calibration surface, extract features, and adjust calibration parameters autonomously, eliminating the need for separate calibration equipment or manual procedures.
Solution Approach 2:
The calibration process dynamically adjusts transformation parameters between sensor coordinate systems based on detected features. The system modifies calibration transforms by comparing expected positions of calibration surface features with actual detected positions, then optimizes parameters to minimize alignment errors between 2D camera and 3D depth sensor data.
2Measurement precision
If multiple sensors are used to capture comprehensive image information, then measurement precision is improved, but computational burden increases
Solution Approach 1:
The system merges data from the 2D camera and 3D depth sensor into a unified calibrated coordinate system, allowing object identification to leverage both color/texture information and depth/geometry information simultaneously. By integrating sensor data in a calibrated framework, the system achieves more accurate object recognition than either sensor could provide alone.
Solution Approach 2:
The patent replaces complex mechanical calibration procedures with computational methods. Instead of physically adjusting sensor positions or using specialized calibration equipment, the system uses image processing and feature matching algorithms to compute calibration transforms, reducing mechanical complexity while maintaining precision.
3Ease of operation
If sensors are calibrated statically before operation, then ease of operation is improved, but reliability deteriorates when sensors become offset during use
Solution Approach 1:
The calibration process operates continuously or periodically during mobile device operation rather than being a one-time static procedure. The system repeatedly detects calibration surfaces, computes alignment errors, and updates calibration transforms to maintain accuracy as sensors shift during use, ensuring continuous reliability.
Solution Approach 2:
The system uses feedback from detecting calibration surface features to continuously monitor and adjust sensor alignment. By comparing the detected positions of calibration features with their expected positions in the calibrated coordinate system, the system generates feedback signals that drive corrections to calibration transforms, maintaining reliability under varying operating conditions.
Data Source
AI summary
Implementations described and claimed herein provide systems and methods for sensor calibration. In one implementation, an image and a depth map of a field around a mobile device are obtained. The image is captured using a first sensor, and the depth map is captured using a second sensor. A position and a reflected radiation intensity of a calibration surface are detected in the field using the depth map. A first portion and a second portion of the calibration surface representing a background color and a foreground color are isolated based on the reflected radiation intensity. The calibration surface is detected in the image using the position of the calibration surface in the depth map and a calibration transform between the first sensor and the second sensor. The calibration transform is optimized based on an overlap of the foreground color and/or the background color between the depth map and the image.