Time-of-Flight Sensor 3D Warping for Accurate Range-Rate
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Solution Overview
Problem
Time-of-flight sensors in autonomous vehicles face challenges due to pixel misalignment between frames caused by motion, leading to errors in depth estimation and range-rate calculations.
Innovation Solution
The system calculates optical flow among image frames using an optical flow constraint equation that incorporates depth information to align images in three dimensions, mitigating motion artifacts and improving depth map accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If time-of-flight sensors are used to measure distance and velocity, then navigation capability is improved, but pixel misalignment between frames causes measurement precision to deteriorate
Solution Approach 1:
The system performs preliminary actions by capturing multiple image frames before final depth estimation. By collecting a sequence of frames and calculating optical flow between them, the system prepares alignment information in advance that compensates for motion-induced pixel misalignment, thereby improving depth measurement precision without sacrificing navigation capability
Solution Approach 2:
The system uses feedback by calculating optical flow between consecutive frames and using this information to adjust subsequent depth estimation processes. The optical flow data provides feedback about motion patterns that helps correct pixel misalignment in real-time, maintaining measurement precision while preserving the sensor's full navigation functionality
2Measurement precision
If multiple image frames are processed to improve depth accuracy, then measurement precision is improved, but processing time increases
Solution Approach 1:
The system applies partial action by selectively processing only the necessary portions of multiple frames. Instead of fully processing all frames independently, it calculates optical flow to identify and correct only the motion-induced misalignment components, achieving improved depth accuracy while minimizing additional processing time
Solution Approach 2:
The system changes parameters by transitioning from independent frame processing to correlated processing using optical flow constraints. This parameter change allows the system to leverage temporal information across frames for improved accuracy while maintaining processing efficiency through the use of constraint equations that reduce computational complexity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the precision of range and range-rate determination by correcting pixel misalignment, thereby improving the navigation and control systems of autonomous vehicles.
Implementation Method 1
depth information from a time-of-flight sensor
Data Source
AI summary
Systems and techniques are provided for determining range and range-rate in time-of-flight sensors. An example method includes determining a first depth map that is based on a first image frame and a second image frame, wherein the first image frame and the second image frame correspond to a first set of image frames received from a time-of-flight sensor; calculating, based on the first depth map, a first set of three-dimensional optical flow data between the first image frame and the second image frame; performing three-dimensional warping of at least one image frame from the first set of image frames using the first set of three-dimensional optical flow data to yield a first realigned image frame; and determining a second depth map that is based on the first realigned image frame.