3D Reconstruction via Monocular Camera and Laser Fusion
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Solution Overview
Problem
Generating accurate three-dimensional reconstructions of environments for autonomous vehicles and machines is challenging due to the need for complex and expensive hardware or complex calibration procedures.
Innovation Solution
A system and method that combines a monocular camera with a single beam laser distancer and utilizes a machine learning model, such as a convolutional neural network, to generate three-dimensional reconstructions by fusing image sequences with distance values, while compensating for angular offsets between the camera and laser distancer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex hardware systems are used for three-dimensional reconstruction, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent combines a monocular camera and a laser distancer into a single integrated system for three-dimensional reconstruction. The camera captures image sequences while the laser distancer provides distance measurements, and both data streams are fused through a machine learning model to generate accurate 3D reconstructions. This merging allows the system to achieve measurement precision comparable to complex hardware systems while maintaining simpler, more cost-effective components.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary that processes and fuses data from the camera and laser distancer. This intermediary component learns to associate image sequences with distance values and generates three-dimensional models without requiring complex calibration procedures. The machine learning model acts as a mediator that translates simple sensor inputs into accurate three-dimensional reconstructions, eliminating the need for complex hardware systems.
2Measurement precision
If complex calibration procedures are performed, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs self-calibration through the machine learning model, which automatically learns the relationship between camera images and laser distance measurements during the data collection phase. The model is trained to associate image sequences with corresponding distance values, effectively calibrating the system without requiring manual intervention or complex calibration procedures. This self-service approach eliminates time-consuming calibration steps while maintaining measurement precision.
Solution Approach 2:
The patent performs calibration actions preliminarily during the machine learning model training phase. By collecting image sequences and distance values together during normal operation and training the model on this paired data, the system pre-establishes the calibration relationship before actual three-dimensional reconstruction begins. This preliminary calibration eliminates the need for time-consuming calibration procedures during deployment.
3Measurement precision
If expensive hardware is used, then measurement precision is improved, but loss of substance increases
Solution Approach 1:
The patent replaces expensive, complex hardware systems with simpler, more affordable components - specifically a monocular camera and a single beam laser distancer. These cheaper components are used in conjunction with a machine learning model to achieve three-dimensional reconstruction accuracy that would traditionally require expensive hardware. The system sacrifices the need for costly specialized sensors by using readily available, inexpensive components combined with intelligent processing.
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 approach allows for the generation of accurate three-dimensional reconstructions with scale information, reducing the need for expensive hardware and complex calibration procedures, and enabling effective navigation for autonomous vehicles and machines.
Implementation Method 1
the distance sensor may be a single beam laser distancer including a laser emitter and a laser receiver
Implementation Method 2
a single beam laser distancer including a laser emitter and a laser receiver
Implementation Method 3
an image sensor and a laser distancer, the image sensor directed in a first direction and the laser distancer directed in a second direction
Data Source
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AI summary
Disclosed are methods and systems for generating three-dimensional reconstructions of environments. A system, for example, may include a housing having an image sensor directed in a first direction and a distance sensor directed in a second direction and a control unit including a processor and a memory storing instructions. The processor may be configured to execute the instructions to: generate a first 3D model of an environment; generate a plurality of revolved 3D models by revolving the first 3D model relative to the image sensor to a plurality of positions within a predetermined angular range; match a set of distance values to one of the revolved 3D models; determine an angular position of the second direction relative to the first direction; and generate a 3D reconstruction of the environment.