PPE Sensor Fusion for Smoke-Obscured Navigation and Mapping
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Solution Overview
Problem
Emergency workers in hazardous environments face difficulties in navigation due to impaired visibility and audibility caused by conditions like smoke or extreme thermal events, which hinder conventional camera-based systems.
Innovation Solution
Personal protective equipment (PPE) systems that integrate sensors and computing devices for real-time or near-real-time data processing, using speech input, radar, thermal imaging, and fiducial markers to construct and refine maps, providing navigation and assistance in visually and auditorily obscured environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional camera-based systems are used for navigation, then the system is simple and easy to operate, but the system fails in visually obscured environments such as smoke or debris
Solution Approach 1:
The patent combines multiple sensing modalities (camera, radar, LIDAR, inertial sensors, acoustic sensors) into an integrated navigation system. This merging allows the system to maintain reliability in visually obscured environments by switching between or fusing data from different sensors, resolving the contradiction between navigation reliability and system complexity through multi-sensor integration.
Solution Approach 2:
The patent introduces intermediate processing layers including sensor fusion algorithms, SLAM (Simultaneous Localization and Mapping) processors, and data association modules that mediate between raw sensor data and navigation decisions. These intermediaries enable the system to handle complex multi-sensor data while maintaining operational simplicity for the end user, thus resolving the contradiction between reliability and ease of operation.
2Measurement precision
If multiple sensors are integrated for real-time map construction, then navigation accuracy is improved, but data processing complexity and computational requirements increase
Solution Approach 1:
The patent segments the data processing pipeline into distinct functional modules: sensor data acquisition, preprocessing, feature extraction, SLAM processing, map construction, and navigation decision-making. This segmentation allows each module to handle specific tasks efficiently, reducing overall processing complexity while maintaining high location accuracy through specialized processing at each stage.
Solution Approach 2:
The patent performs preliminary processing of sensor data including calibration, synchronization, and feature detection before main SLAM processing. By preparing data in advance and pre-computing transformation matrices and coordinate systems, the system reduces real-time computational burden while maintaining measurement precision during active navigation.
3Ease of operation
If speech input and gesture recognition are added to augment navigation control, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal control interface that accepts multiple input modalities (speech, gestures, traditional controls) and translates them into unified navigation commands. This multi-functional approach improves ease of operation by allowing users to choose their preferred interaction method while the underlying system maintains a single, standardized control architecture, thus managing complexity through interface abstraction.
Solution Approach 2:
The system incorporates automatic speech recognition and gesture interpretation capabilities that require minimal programming or configuration. The control system automatically adapts to user inputs, performs natural language processing, and maps gestures to navigation commands without requiring complex manual programming, thereby improving ease of operation while keeping the control system complexity manageable through self-configuration algorithms.
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
Enables accurate and resilient navigation and mapping in hazardous conditions, enhancing situational awareness and response times for emergency personnel by integrating sensor data and speech inputs to generate precise location and environmental feature information.
Implementation Method 1
a radar device configured to generate radar data including coarse-grain information indicating a presence or arrangement of objects within the visually obscured environment
Implementation Method 2
a thermal image capture device configured to generate thermal image data
Implementation Method 3
an inertial measurement device configured to generate inertial data
Data Source
AI summary
A system includes a personal protective equipment (PPE) configured to be worn by an agent. The PPE includes a sensor assembly comprising a radar device configured to generate radar data, a microphone configured to capture speech input from the agent, and an inertial measurement device configured to generate inertial data. The system includes a computing device configured to: process sensor data from the sensor assembly, the sensor data including at least the radar data and the inertial data, to generate pose data of the agent based on the processed sensor data, the pose data including a location and an orientation of the agent as a function of time, to process the speech input to identify an item of interest in an environment in which the PPE is deployed, and to form mapping information for the environment with the item of interest being marked, based on the processed speech input.