Time-of-Flight Sensor Precision Prediction Using Single-Frame Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Time-of-flight (TOF) sensors face inefficiencies in determining precision of measurements, particularly in dynamic applications like autonomous vehicles, as current methods require evaluating multiple frames to assess signal noise and reliability, which is time-consuming and may lose dynamic values.
Innovation Solution
The system determines precision and reliability of TOF measurements based on data from a single frame by generating a confidence map that represents the estimated precision per pixel, using a combination of depth data and grayscale data to account for noise from both active and ambient light sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple frames are evaluated to determine TOF measurement precision, then measurement reliability is improved, but processing time increases and dynamic values are lost
Solution Approach 1:
The system performs preliminary calculations by pre-computing noise parameters (ambient light noise and active light noise) from the received signal. These pre-computed noise values are then used in the precision prediction formula, allowing single-frame precision determination without requiring multiple frame evaluations, thus resolving the contradiction between reliability and processing time
Solution Approach 2:
The system uses the TOF sensor's own received signal to extract noise parameters and predict measurement precision. By utilizing the signal already captured by the sensor rather than requiring additional frames or external reference signals, the system achieves reliable precision estimation from a single frame, eliminating the time loss associated with multiple frame evaluations
2Measurement precision
If multiple frames are evaluated to assess signal noise, then noise characterization accuracy is improved, but the system loses dynamic values and real-time responsiveness
Solution Approach 1:
The system performs preliminary extraction of noise parameters (ambient light noise and active light noise) from the received signal within the same frame used for depth measurement. This preliminary noise characterization enables real-time precision prediction without requiring additional frames, thus maintaining both noise characterization accuracy and real-time responsiveness
Solution Approach 2:
The system segments the received signal into distinct noise components (ambient light noise and active light noise) that can be independently characterized and combined. This segmentation allows accurate noise modeling from a single frame by separating different noise sources, achieving both accurate noise characterization and real-time processing
3Productivity
If single frame data is used for precision prediction, then real-time performance is improved, but measurement reliability may be compromised
Solution Approach 1:
The system changes the approach from using multiple frames (temporal dimension) to using multiple parameters from a single frame (spatial/spectral dimension). By extracting both ambient light noise and active light noise parameters from the same frame and combining them in a precision prediction formula, the system achieves real-time performance while maintaining measurement reliability through multi-parameter analysis
4Measurement precision
If traditional multi-frame evaluation is used, then comprehensive noise assessment is achieved, but system complexity and processing overhead increase
Solution Approach 1:
The system performs preliminary extraction and storage of noise parameters (ambient light noise and active light noise) from the received signal. These pre-computed parameters are then directly substituted into a closed-form precision prediction equation, eliminating the need for complex multi-frame processing algorithms and reducing computational complexity while maintaining noise assessment accuracy
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 allows for real-time, accurate prediction of TOF measurement precision, enhancing the safety and functionality of autonomous vehicles by enabling them to accurately detect distances and navigate environments without relying on multiple frame evaluations.
Implementation Method 1
depth data associated with a frame, the depth data being captured by a time-of-flight (TOF) camera by illuminating a modulated signal of the TOF camera and receiving a reflected signal at the TOF camera
Data Source
AI summary
Systems and techniques are provided for predicting precision of time-of-flight (TOF) sensor measurements. An example process includes receiving depth data associated with a frame, the depth data being captured by a TOF camera by illuminating a modulated signal of the TOF camera and receiving a reflected signal at the TOF camera, wherein the depth data comprises a plurality of correlation samples based on the reflected signal; receiving, from the TOF camera, grayscale image data corresponding to the frame; determining a modulation amplitude of the modulated signal based on the plurality of correlation samples; determining one or more parameters associated with at least one of the depth data and the grayscale image data; and determining a reliability of the depth data based on the modulation amplitude of the modulated signal, the grayscale image data, and the one or more parameters.