Pseudo LiDAR Navigation Using Sparse Maps for Vehicle Localization
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating effectively due to the vast amounts of data they need to process and store, particularly with traditional mapping technologies, which can limit their ability to accurately identify location, navigate through complex environments, and make real-time decisions.
Innovation Solution
The implementation of a system that utilizes cameras and LIDAR systems to provide navigation features, including the use of sparse maps generated from drive information collected by multiple vehicles, allowing for efficient data storage and processing, and enabling vehicles to determine ego motion, object velocity, and navigational actions based on point cloud information and image analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional mapping technology is used for autonomous vehicle navigation, then comprehensive environmental information can be obtained, but data storage requirements and processing complexity increase significantly
Solution Approach 1:
The patent extracts only the essential navigational elements from the complete environmental map, creating a sparse map that contains only road boundaries, lane markings, and key landmarks needed for navigation. This extraction process removes unnecessary data while preserving critical information for autonomous driving decisions.
Solution Approach 2:
The navigation system divides the environmental information into hierarchical segments: complete maps for long-term storage and sparse maps for real-time navigation. This segmentation allows the system to maintain comprehensive environmental knowledge while using only essential data during active navigation, reducing processing load and storage requirements.
2Measurement precision
If traditional mapping technology is used for autonomous vehicle navigation, then accurate location identification is possible, but processing time and computational resources increase
Solution Approach 1:
The system performs partial processing by using sparse maps for real-time location identification instead of processing complete environmental maps. This partial action approach processes only the essential navigational data needed for current location determination, significantly reducing computational time while maintaining sufficient accuracy for navigation purposes.
3Reliability
If comprehensive map data is stored and updated continuously, then navigation accuracy is maintained, but system complexity and update requirements increase
Solution Approach 1:
The system extracts only the critical navigational features from complete maps to create sparse representations. This extraction maintains navigation reliability by preserving essential road geometry and landmark information while eliminating redundant data that would increase system complexity and update requirements.
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 enables autonomous vehicles to navigate more efficiently by reducing data storage needs, improving localization, and allowing for real-time decision-making, enhancing their ability to safely traverse roads and interact with obstacles and traffic signals.
Implementation Method 1
receive, from a LIDAR system associated with the host vehicle and based on a first LIDAR scan of a field of view of the LIDAR system, a first point cloud including a first representation of at least a portion of an object
Data Source
AI summary
A navigation system for a host vehicle may include a processor programmed to: receive from a center camera onboard the host vehicle a captured center image including a representation of at least a portion of an environment of the host vehicle, receive from a left surround camera onboard the host vehicle a captured left surround image including a representation of at least a portion of the environment of the host vehicle, and receive from a right surround camera onboard the host vehicle a captured right surround image including a representation of at least a portion of the environment of the host vehicle; provide the center image, the left surround image, and the right surround image to an analysis module configured to generate an output relative to the at least one captured center image; and cause a navigational action by the host vehicle based on the generated output.