Histogram Processing for Time-of-Flight Depth Measurement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Time-of-flight imaging systems face challenges in accurately processing histograms of arrival times to determine depth and distance due to issues like cross-talk interference and low signal-to-noise ratios, especially when dealing with close targets and overlapping histogram signatures.
Innovation Solution
The system employs a processor to iteratively process the histogram using an expectation-maximization algorithm, which includes optical emitters and detectors to emit and receive radiation, generating a histogram indicative of time differences, and compensates for cross-talk to accurately determine the presence and distance of objects by segmenting pulses and estimating parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional histogram processing methods are used, then the device complexity is low, but the measurement precision deteriorates due to cross-talk interference and low signal-to-noise ratios
Solution Approach 1:
The patent segments the histogram processing into multiple iterative steps using the expectation-maximization algorithm. The algorithm divides the histogram into multiple Gaussian components, each representing a potential target, and iteratively refines the parameters (amplitude, mean, standard deviation) of each component to separately identify multiple targets even when their signatures overlap
Solution Approach 2:
The patent introduces an intermediate statistical model (Gaussian mixture model) as a mediator between the raw histogram data and the final target detection. This intermediate representation allows the system to handle cross-talk interference and low signal-to-noise ratios by modeling the underlying probability distribution of photon arrival times
2Measurement precision
If simple peak detection is used, then the processing speed is high, but the measurement precision deteriorates in low signal-to-noise scenarios
Solution Approach 1:
The patent employs periodic iterative refinement through the expectation-maximization algorithm, which cycles between estimating parameters and maximizing likelihood until convergence. This periodic action allows the system to progressively improve detection accuracy while providing a convergence criterion to limit processing time
Solution Approach 2:
The algorithm performs self-service by automatically adapting to the signal-to-noise conditions and converging to the optimal parameter estimates without requiring manual intervention or tuning. The iterative process self-regulates, spending more computational effort when needed and converging when sufficient accuracy is achieved
3Reliability
If conventional histogram processing is used, then the ease of operation is high, but the reliability deteriorates when detecting close targets with overlapping signatures
Solution Approach 1:
The patent segments the overlapping histogram signatures into distinct Gaussian components, each representing a separate target. This segmentation allows the system to reliably detect and measure multiple close targets by treating them as separate entities with their own parameters, even when their photon arrival time distributions overlap significantly
Solution Approach 2:
The patent changes the parameter representation from simple peak locations to full Gaussian distributions characterized by amplitude, mean, and standard deviation. This parameter expansion allows the system to capture the shape and spread of photon arrival times, providing more information for reliable target detection and discrimination
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 enables effective detection and discrimination of close targets with overlapping signatures and improves target detection in low signal-to-noise scenarios, providing accurate range measurements and enhancing the robustness of time-of-flight technology.
Implementation Method 1
a plurality of optical emitters configured to emit incident radiation within a field of view of the device
Implementation Method 2
a plurality of optical detectors configured to receive reflected radiation
Implementation Method 3
a plurality of optical detectors configured to receive reflected radiation and to generate a histogram based on the incident radiation and the reflected radiation
Data Source
AI summary
An example device has optical emitters for emitting incident radiation within a field of view and optical detectors for receiving reflected radiation. Based on the incident radiation and the reflected radiation, a histogram indicative of a number of photon events that are detected by the optical detectors over time bins is generated. The time bins is indicative of time differences between emission of the incident radiation and reception of the reflected radiation. The device further includes; a processor programmed to iteratively process the histogram by executing an expectation-maximization algorithm to detect a presence of objects located in the field of view of the device.