Multisensor ROI Selection for Low-Latency Feature Detection
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Solution Overview
Problem
Autonomous vehicles and robotic devices face challenges in processing and transferring large amounts of sensor data from multiple high-resolution sensors, leading to data transfer latency and bandwidth limitations, which hinder timely detection and analysis of environmental features.
Innovation Solution
Implementing a system that divides the field of view of sensors into regions of interest (ROIs) allowing for selective data readout and peer-to-peer communication among sensors to efficiently share information and reduce latency, enabling accurate feature detection and attribute analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full-resolution sensor data is obtained from multiple high-resolution sensors, then measurement precision is improved, but data transfer latency increases and bandwidth is consumed
Solution Approach 1:
The field of view of each sensor is divided into multiple regions of interest (ROIs). Instead of transmitting entire high-resolution sensor data, only the data from selected ROIs that contain detected features of interest are transmitted and processed. This segmentation reduces the volume of data transfer while maintaining detection accuracy for relevant features.
Solution Approach 2:
The system extracts and transmits only the essential portions of sensor data - specifically the ROIs containing features of interest - rather than transmitting complete full-resolution data from all sensors. This extraction approach eliminates unnecessary data transmission while preserving critical information needed for accurate feature detection.
2Measurement precision
If full-resolution sensor data is processed from all sensors, then measurement precision is improved, but device complexity increases
Solution Approach 1:
Processing is segmented to focus only on ROIs containing features of interest. Each sensor divides its field of view into multiple ROIs, and the system processes only those ROIs that contain detected features, rather than processing entire high-resolution data from all sensors. This reduces computational complexity while maintaining detection accuracy.
Solution Approach 2:
The system extracts only the necessary data portions (ROIs with features of interest) for processing, eliminating the need to process redundant full-resolution data from all sensors. This extraction strategy reduces processing complexity by focusing computational resources on relevant data only.
3Ease of operation
If central control system coordinates sensor data sharing, then system management is simplified, but communication delay increases
Solution Approach 1:
Detected features of interest act as intermediaries that trigger ROI selection and data sharing between sensors. When a feature is detected by one sensor, it serves as the basis for selecting corresponding ROIs in other sensors, enabling coordinated data acquisition without requiring continuous central control intervention for each data exchange.
Solution Approach 2:
The system performs preliminary detection of features of interest using initial sensor data before selecting and acquiring additional ROI data from other sensors. This preliminary action enables proactive ROI selection based on already-detected features, reducing the need for reactive central coordination and minimizing communication delays.
Data Source
AI summary
A system may include a first sensor of a first type and a second sensor of a second different type and having a detector. A field of view of the second sensor may be formed by a plurality of regions of interest (ROIs) defined by the detector. Control circuitry of the system may be configured to perform operations including obtaining, from the first sensor, first sensor data representing an environment, and determining, based on the first sensor data, information associated with a feature of interest within the environment. The operations may also include determining, based on the information, a particular ROI that corresponds to an expected position of the feature at a later time, obtaining a plurality of ROI sensor data from the particular ROI instead of obtaining full-resolution sensor data, and analyzing the plurality of ROI sensor data to determine one or more attributes of the feature.