3D Scene Acquisition via Dimensionality-Reduced Sparse Representation
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Solution Overview
Problem
Traditional three-dimensional scene information acquisition methods require a large number of sensors, are sensitive to noise, and struggle to detect multiple objects simultaneously, especially in indoor environments where sensor deployment is inconvenient, leading to high costs and poor extensibility.
Innovation Solution
An active three-dimensional scene information acquisition method based on dimensionality-reduced sparse representation, utilizing a transmitter and multiple one-dimensional detection signal receivers, which processes multi-channel detection signals to achieve three-dimensional positioning of multiple targets or scene reconstruction by transforming the problem into a convex optimization model, and further adapts to noisy conditions through dimensionality reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a large number of sensors are deployed in a grid pattern to detect multiple objects, then the coverage and detection capability are improved, but the arrangement cost and device complexity increase significantly
Solution Approach 1:
The patent transforms the traditional two-dimensional sensor array deployment into a three-dimensional positioning system using time-of-flight measurements. By measuring the arrival time of signals at multiple receivers, the system calculates three-dimensional coordinates (x, y, z) of targets, eliminating the need for dense grid-pattern sensor deployment while achieving comprehensive scene coverage and multi-object detection capability.
2Measurement precision
If traditional three-point positioning method is used to acquire three-dimensional position information, then the positioning accuracy is improved, but the number of sensors required increases
Solution Approach 1:
The patent makes each receiver serve multiple functions: it not only detects signal arrival time for positioning but also contributes to scene reconstruction and multi-object detection. By having each receiver perform multiple roles and combining information from all receivers through sparse representation, the system achieves accurate three-dimensional positioning with fewer sensors than the traditional three-point method requires.
3Productivity
If multiple-input multiple-output radar systems are deployed to detect multiple targets simultaneously, then the detection efficiency is improved, but the directionality requirements and system complexity increase
Solution Approach 1:
The patent replaces complex mechanical MIMO radar systems with multiple transmitters and receivers requiring precise directional control, with a simpler system using one transmitter and multiple omnidirectional receivers. The complexity is shifted from hardware configuration to signal processing algorithms, specifically sparse representation and convex optimization, which enable simultaneous multi-target detection without stringent directional requirements.
4Ease of operation
If RFID-based positioning is used in indoor scenes, then the deployment convenience is improved, but the positioning accuracy and multi-object detection capability deteriorate
Solution Approach 1:
The patent introduces time-of-flight measurement as an intermediary mechanism between the simple RFID-like deployment and accurate positioning. By measuring the time it takes for signals to travel from the transmitter to receivers and reflecting off targets, the system achieves precise three-dimensional positioning and multi-object detection while maintaining the deployment simplicity of carrying devices, without requiring dense sensor grids.
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 method enables efficient, cost-effective, and extensible three-dimensional scene information acquisition, breaking the sensor limitation of traditional methods and effectively handling high noise levels, allowing for simultaneous positioning of multiple targets and scene reconstruction with fewer sensors.
Implementation Method 1
analyzing time when a target echo reaches different measurement points
Implementation Method 2
a waveform of a single reflected signal received by each receiver is recorded
Data Source
AI summary
An active three-dimensional scene information acquisition method based on dimensionality-reduced sparse representation is provided. The method jointly processes multiple one-dimensional active detection signals collected synchronously to achieve three-dimensional positioning of objects in a detected scene or three-dimensional reconstruction of a scene structure. Through an active detection system equipped with one transmitter and multiple receivers, simultaneous three-dimensional positioning of multiple targets in a scene or three-dimensional reconstruction of the geometry of the scene is achieved.