ToF Camera Data Synchronization for Low-Latency Stream Encoding
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Solution Overview
Problem
Existing ToF camera systems face challenges in processing and synchronizing data from multiple sensors like ToF, RGB, and IMU efficiently, leading to latency and delayed response times in applications requiring real-time data processing and action.
Innovation Solution
A device and method for data transmission that includes a processor to strip and combine data from multiple sensors (ToF, RGB, and IMU) into a combined data stream, using an encoder to generate an output stream for transmission to a host processor, reducing processing load on the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from multiple sensors (ToF, RGB, IMU) is processed and synchronized in conventional ToF camera systems, then data completeness and accuracy are improved, but processing latency and response time increase
Solution Approach 1:
The patent segments the data processing task by separating synchronization processing into two stages: (1) embedded processor performs preliminary synchronization and encoding of data from multiple sensors, and (2) host processor performs final reconstruction. This division allows critical time-sensitive synchronization to occur at the sensor level, reducing overall latency while maintaining data accuracy.
Solution Approach 2:
The embedded processor performs preliminary actions by pre-synchronizing and pre-encoding data from multiple sensors before transmission to the host processor. This preliminary processing reduces the computational burden on the host processor and enables faster response times by having data ready in a processed state.
2Loss of information
If all sensor data is transmitted and processed in detail, then data completeness is improved, but processing load and computational power requirements increase
Solution Approach 1:
The patent extracts only the essential synchronization information and key data elements from each sensor stream using selective encoding. The embedded processor identifies and transmits only the most critical data components needed for real-time processing, reducing the overall data volume while preserving essential information completeness.
Solution Approach 2:
The system applies partial processing by performing essential synchronization and encoding operations on the embedded processor, then transmits a reduced but sufficient data set to the host processor. This partial action approach maintains data completeness for critical functions while reducing overall computational requirements.
3Device complexity
If data processing is performed at the host processor level, then system complexity is reduced, but response time and real-time capability deteriorate
Solution Approach 1:
The patent adds a new dimension to the processing architecture by introducing an embedded processor at the sensor level, creating a hierarchical processing structure. This dimensional change allows time-critical operations to occur at the embedded level while the host processor handles higher-level tasks, achieving both real-time responsiveness and manageable system complexity.
Solution Approach 2:
The embedded processor acts as an intermediary between the multiple sensors and the host processor. It performs intermediate synchronization and encoding operations, mediating the data flow and reducing the direct processing burden on the host processor while maintaining real-time response capabilities.
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
Minimizes latency and enhances data processing efficiency, allowing for real-time data synchronization and accurate image reconstruction, thereby improving system responsiveness and reducing computational power requirements.
Implementation Method 1
The operational principle of Time of Flight (ToF) cameras is the emission of a light source and the subsequent reception of the reflected light. By calculating the depth through the analysis of reflected light intensity and the time required for the light to travel back to the camera, ToF cameras facilitate accurate distance measurements.
Data Source
AI summary
The invention discloses a device enabling data transmission in a network communication. The device comprises a plurality of sensors and a processor. Each sensor from the plurality of sensors is configured for sensing the data in a predefined area. The processor in the device further comprises an encoder and a transmitter. The processor is configured to strip a first line of data from the predefined data received from the sensors and combine strip data to obtain a combined data stream. The encoder is configured to generate an output data stream by using the combined data stream. The transmitter is configured for transmitting the output data stream to a host processor for reconstructing an image form the output data stream.