Mono Camera and LiDAR Fusion for Real-Time Vehicle Localization
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Solution Overview
Problem
Current autonomous driving methods require expensive and complex equipment like high-precision GPS receivers and stereo cameras to accurately estimate vehicle location and distance, which increases costs and complicates data processing, limiting real-time reliability and accuracy.
Innovation Solution
A method using a mono camera and LiDAR to estimate distance and location by acquiring two-dimensional video images, correcting lens distortions, matching coordinate systems, and estimating object distances through LiDAR data, reducing data processing and eliminating the need for expensive equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-precision GPS receiver is used to achieve small error range for accurate vehicle location, then location accuracy is improved, but device cost increases
Solution Approach 1:
The patent uses multiple low-cost GPS receivers instead of one high-precision GPS receiver. Each GPS receiver is inexpensive and can be replaced if needed, but collectively they provide high-precision location data through geometric complementation, resolving the contradiction between cost and accuracy
Solution Approach 2:
The patent combines data from multiple GPS receivers with camera image data and LiDAR data to achieve accurate vehicle location and distance estimation. By merging these different data sources, the system achieves high measurement precision without relying on expensive high-precision GPS equipment
2Measurement precision
If multiple GPS receivers are used to secure precise location data, then location accuracy is improved, but device configuration complexity increases
Solution Approach 1:
The multiple GPS receivers are not only used for location data but also work in conjunction with camera and LiDAR systems. The GPS data serves multiple purposes including vehicle positioning, distance estimation, and environmental mapping, reducing overall system complexity through multi-functionality
Solution Approach 2:
The patent introduces a coordinate system matching process as an intermediary that seamlessly integrates GPS data with camera and LiDAR data. This mediator component handles the complexity of data fusion, making the overall system configuration more manageable despite using multiple sensors
3Reliability
If multiple GPS receivers are interconnected to complement location information, then location reliability is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing into distinct modules: GPS data processing, camera image processing, LiDAR data processing, and coordinate system matching. Each module handles specific data independently before integration, reducing the complexity of processing multiple GPS receivers' data while maintaining reliability
4Measurement precision
If stereo camera is used to acquire surrounding environment information, then depth measurement accuracy is improved, but device cost increases
Solution Approach 1:
The patent uses a mono camera instead of an expensive stereo camera system. By combining the mono camera with LiDAR data and GPS information, the system achieves accurate depth and distance measurements at lower cost, replacing costly stereo cameras with a more economical sensor combination
5Loss of information
If stereo camera is used for autonomous driving, then surrounding environment information is improved, but device configuration complexity increases
Solution Approach 1:
The patent merges data from mono camera, LiDAR, and GPS receivers to achieve comprehensive surrounding environment information. This combination replaces the stereo camera configuration, reducing device complexity while maintaining or improving information quality through multi-sensor fusion
6Measurement precision
If image data amount is increased to improve accuracy, then measurement accuracy is improved, but real-time processing capability deteriorates
Solution Approach 1:
The patent extracts only the essential features from camera images rather than processing complete high-resolution images. By extracting key visual information and combining it with precise LiDAR and GPS data, the system achieves high measurement accuracy while maintaining real-time processing capability through reduced data volume
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 efficiently acquires reliable information in real-time with high accuracy, minimizing data processing time and reducing errors, while avoiding the costs and complexities associated with high-precision GPS and stereo cameras.
Implementation Method 1
acquire a two-dimensional video image captured by the mono camera; acquire ground data measured by a three-dimensional LiDAR
Data Source
AI summary
Proposed is a method for estimating the distance to and the location of an autonomous vehicle by using a mono camera, and more particularly, a method that enables information necessary for autonomous travel to be efficiently acquired using a mono camera and LiDAR. In particular, the method can acquire sufficiently reliable information in real time without using expensive equipment, such as a high-precision GPS receiver or stereo camera, required for autonomous travel. Consequently, the method may be widely used in ADAS, such as for semantic information recognition for autonomous travel, estimation of the location of an autonomous vehicle, calculation of vehicle-to-vehicle distance, or the like, even without the use of GPS, and furthermore, a camera capable of performing the same functions can be developed by developing software through the use of the corresponding data.