Unified Sensor Fusion for Accurate Environmental Mapping
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Solution Overview
Problem
Existing environmental mapping systems, such as LIDAR and vision systems, face limitations in adverse weather conditions and sensor contamination, leading to unreliable localization and mapping in various environments.
Innovation Solution
A unified mapping system that combines radar, LIDAR, and vision data to generate accurate maps, using a processor to determine the most accurate data source based on environmental conditions, thereby creating a robust and reliable map of the environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR systems are used for high resolution mapping, then measurement precision is improved, but reliability deteriorates in adverse weather conditions
Solution Approach 1:
The patent combines LIDAR, radar, and vision sensor systems into a unified mapping system. The LIDAR provides high precision range data, radar provides all-weather capability, and vision provides color information. The system fuses these complementary data sources to achieve both high precision and reliability across all weather conditions.
Solution Approach 2:
The patent creates a composite sensing system that integrates multiple sensor types (LIDAR, radar, vision) similar to how composite materials combine different materials to achieve properties that individual materials cannot provide alone. Each sensor type contributes its strengths to the overall system performance.
2Ease of manufacture
If vision systems are used for mapping, then cost is reduced and color information is provided, but measurement precision and reliability deteriorate
Solution Approach 1:
The patent merges vision systems with LIDAR and radar systems. The vision system provides cost-effective color information and basic environmental data, while LIDAR and radar compensate for the lack of precise range estimation capability in vision systems alone.
3Reliability
If radar systems are used for mapping, then reliability in adverse weather is improved, but measurement precision deteriorates
Solution Approach 1:
The patent combines radar systems with LIDAR systems. The radar provides reliable detection in adverse weather conditions, while the LIDAR provides high precision range data when conditions permit, creating a system that maintains both reliability and precision.
4Adaptability or versatility
If a combination of LIDAR and vision systems is used, then mapping capabilities are enhanced, but vulnerability to weather conditions persists
Solution Approach 1:
The patent merges LIDAR and vision systems with radar systems. This combination adds all-weather capability to the enhanced mapping capabilities already provided by LIDAR and vision, eliminating the weather vulnerability while preserving the enhanced mapping functions.
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
The system provides enhanced mapping capabilities in adverse weather conditions by leveraging the strengths of radar, LIDAR, and vision systems, resulting in improved accuracy and reliability for autonomous vehicles and other applications.
Implementation Method 1
A scanning radar, or combination of radars, that scans the surrounding environment
Implementation Method 2
receive radar map data about the environment from the radar system
Implementation Method 3
LIDAR uses ultraviolet, visible or near infrared light to image objects
Implementation Method 4
LIDAR uses ultraviolet, visible or near infrared light to image objects
Implementation Method 5
Vision systems use visible light to image objects, are cheaper than LIDAR systems and can provide color information about an environment
Data Source
AI summary
A method and system for generating a map of an environment based on information acquired by radar combined with information acquired from LIDAR, cameras, or a combination of the LIDAR and camera. The system uses a combination of data from a radar system combined with data from one or both of a camera system and LIDAR system to generate a unified map of the environment.