Sensor Data Temporal Alignment via Timestamp Intermediaries
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Solution Overview
Problem
Existing systems fail to effectively combine and align sensor data from different sensors with varying operation rates, such as image, depth, and inertial sensors, which are often temporally misaligned, making it challenging to generate coherent composite data sets.
Innovation Solution
A system and method that utilize hardware processors configured by machine-readable instructions to capture and combine chromatic information from image sensors, depth information from depth sensors, and motion parameters from inertial sensors, interpolating motion parameters and re-projecting depth images to align data as if captured simultaneously, thereby generating composite data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If sensor data from different sensors with varying operation rates are combined directly, then data integration is simplified, but temporal misalignment occurs making coherent composite data generation difficult
Solution Approach 1:
The system performs preliminary actions by capturing and storing sensor data with precise timestamps before combination occurs. Image sensor data, depth sensor data, and inertial sensor data are each captured independently with their respective timestamps, allowing subsequent alignment operations to reference these pre-captured time markers and properly synchronize the heterogeneous data streams.
Solution Approach 2:
The system introduces timestamps as an intermediary element that mediates between sensors operating at different rates. Each sensor data packet is tagged with a timestamp indicating when it was captured, and this timestamp serves as the intermediary reference that enables the fusion system to align and correlate data from image sensors, depth sensors, and inertial sensors despite their varying operation rates.
2Productivity
If sensors operate at different rates to optimize individual sensor performance, then each sensor can function optimally, but aligning their data for coherent composite sets becomes challenging
Solution Approach 1:
The system performs preliminary timestamping on each sensor data capture event. Before attempting to align data, each sensor (image sensor, depth sensor, inertial sensor) independently captures data at its optimal rate and attaches a precise timestamp. This preliminary timestamping action preserves individual sensor performance while enabling subsequent precision alignment through time-based correlation.
Solution Approach 2:
Timestamps serve as the intermediary that bridges sensors operating at different rates. The system uses these timestamp intermediaries to precisely match and align corresponding data points from image sensors, depth sensors, and inertial sensors, achieving high precision data alignment without constraining individual sensor operation rates.
3Device complexity
If data from multiple sensors are combined without temporal alignment, then processing complexity is reduced, but the resulting composite data lacks coherence and accuracy
Solution Approach 1:
The system performs preliminary timestamping on all sensor data before combination, establishing a time reference framework. This preliminary action enables subsequent alignment operations to efficiently match data points based on their timestamps, achieving accurate temporal alignment without requiring complex real-time synchronization during the data fusion process.
Solution Approach 2:
Timestamps act as the intermediary mechanism that enables accurate measurement and alignment of composite data. By using timestamps as the reference intermediary, the system can precisely match data from image sensors, depth sensors, and inertial sensors in time, ensuring high measurement precision and accuracy in the resulting composite data sets.
Data Source
AI summary
Systems and methods for generating composite sets of data based on sensor data from different sensors are disclosed. Exemplary implementations may capture a color image including chromatic information; capture a depth image; generate inertial signals conveying values that are used to determine motion parameters; determine the motion parameters based on the inertial signals; generate a re-projected depth image as if the depth image had been captured at the same time as the color image, based on the interpolation of motion parameters; and generate a composite set of data based on different kinds of sensor data by combining information from the color image, the re-projected depth image, and one or more motion parameters.