Time-of-Flight Sensor Frame Realignment via Optical Flow
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Solution Overview
Problem
Time-of-flight sensor systems in autonomous vehicles face motion misalignment artifacts due to relative motion between the sensor and objects, leading to errors in depth estimation, which conventional approaches struggle to mitigate effectively without compromising signal collection time.
Innovation Solution
The system identifies pairs of non-adjacent frames with similar scene structure, calculates computed optical flow data, and generates estimated optical flow data for intermediate or future frames through interpolation or extrapolation, allowing for frame realignment and improved depth accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the time period over which discrete frames is compressed to minimize relative motion impact, then motion misalignment is reduced, but integration times are shortened leading to less signal being collected
Solution Approach 1:
The system performs preliminary actions by capturing multiple frames over an extended time period with sufficient integration times to collect adequate signal. Motion correction is then applied retrospectively through optical flow analysis and frame realignment, allowing the system to maintain long integration times while still correcting for motion effects in the final depth estimation.
2Measurement precision
If integration times are arbitrarily shortened to reduce motion impact, then relative motion between frames is minimized, but the fundamental signal collection requirement cannot be met
Solution Approach 1:
The system captures frames with sufficient integration times to meet signal collection requirements before motion correction is applied. The preliminary frame capture phase ensures adequate signal, and subsequent optical flow-based realignment corrects the motion artifacts without having compromised signal quality.
Solution Approach 2:
Instead of relying on mechanical timing constraints to prevent motion artifacts, the system substitutes a computational approach using optical flow analysis and digital frame realignment. This replaces the need for mechanically constrained short integration times with a software-based motion correction methodology.
3Measurement precision
If the time from beginning to end of frame capture sequence is compressed, then motion artifacts are reduced, but sensor design limits prevent arbitrary compression due to finite reading time
Solution Approach 1:
The system performs preliminary frame capture with sufficient time for proper sensor operation including reading, resetting, and re-initiating integration. Motion correction is then applied computationally afterward, separating the signal collection phase from the motion correction phase and allowing each to be optimized independently.
Solution Approach 2:
The system replaces mechanical/time-based motion prevention with computational motion correction. Instead of constraining the sensor operation timeline to prevent motion, the system allows extended capture times for proper sensor operation and then uses optical flow algorithms to correct motion effects in software.
4Quantity of substance
If frames are captured over an extended time period, then sufficient signal is collected for each frame, but relative motion causes pixels to be offset between frames
Solution Approach 1:
The system uses computational optical flow analysis and digital image processing to correct motion offsets between frames. Instead of relying on mechanical synchronization or short capture times, the system applies algorithms to estimate and compensate for pixel displacement caused by relative motion during the extended capture period.
Solution Approach 2:
The system introduces optical flow data as an intermediary element that bridges the gap between extended-time frame captures. The optical flow analysis provides motion compensation information that serves as a mediator, allowing frames captured over extended periods to be realigned and combined accurately for depth estimation.
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 reduces motion artifacts, enhances depth accuracy, and increases the signal-to-noise ratio by aligning frames, ensuring measurements correspond to common objects at the same depth, thereby improving the overall image quality.
Implementation Method 1
A time-of-flight sensor system is a device used to measure distance to object(s) in an environment
Implementation Method 2
computed optical flow data can be calculated based on the pair of non-adjacent frames in the stream of frames
Data Source
AI summary
Various technologies described herein pertain to mitigating motion misalignment of a time-of-flight sensor system and/or generating transverse velocity estimate data utilizing the time-of-flight sensor system. A stream of frames outputted by a sensor of the time-of-flight sensor system is received. A pair of non-adjacent frames in the stream of frames is identified. Computed optical flow data is calculated based on the pair of non-adjacent frames in the stream of frames. Estimated optical flow data for at least one differing frame can be generated based on the computed optical flow data, and the at least one differing frame can be realigned based on the estimated optical flow data. Moreover, transverse velocity estimate data for an object can be generated based on the computed optical flow data.