LIDAR-Camera Fusion for Vantage Point Mismatch Detection
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating due to the vast amounts of data they need to process and store, particularly with traditional mapping technologies, which can limit their ability to efficiently analyze and update maps while traveling.
Innovation Solution
The use of cameras and LIDAR systems to provide navigation features, including determining ego motion, object detection, and localization, with sparse maps generated from drive information collected from multiple vehicles, allowing for efficient data storage and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional mapping technology is used to navigate, then navigation accuracy is improved, but data storage requirements and processing complexity increase significantly
Solution Approach 1:
The patent extracts only the essential navigational elements from complete maps, creating sparse maps that contain only critical features needed for navigation decisions. This selective extraction reduces data storage requirements while maintaining navigation accuracy by focusing on the most important spatial information.
Solution Approach 2:
The patent segments the environment into discrete navigational elements and represents them as point clouds with specific features. By dividing the continuous map data into segmented, feature-based representations, the system reduces overall data complexity while preserving essential navigation information.
2Reliability
If complete map data is stored and processed, then navigation reliability is improved, but processing time and computational resources increase
Solution Approach 1:
The system extracts only the necessary navigational features from complete map data, storing and processing only these essential elements. This extraction approach maintains navigation reliability by preserving critical path information while dramatically reducing processing time through decreased data volume.
Solution Approach 2:
The patent performs preliminary processing to identify and extract essential navigational features before actual navigation occurs. By pre-processing and selecting only critical elements in advance, the system ensures navigation reliability is maintained while minimizing real-time processing requirements.
3Loss of information
If LIDAR and camera data are fully integrated, then environmental understanding is improved, but data fusion complexity and processing load increase
Solution Approach 1:
The patent extracts complementary information from LIDAR and camera data separately, identifying unique features from each sensor type. By extracting and fusing only the essential complementary features rather than all available data, the system improves environmental understanding while reducing fusion complexity.
Solution Approach 2:
The system segments LIDAR point cloud data and camera image data into distinct feature categories, processing each segment independently before integration. This segmentation approach maintains comprehensive environmental understanding by preserving all sensor information while reducing overall processing complexity through modular handling.
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
Enables efficient autonomous navigation by reducing data storage needs and improving the ability to analyze and update maps, allowing vehicles to accurately navigate roads and make navigational decisions based on real-time environmental data.
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 camera onboard the host vehicle at least one captured image representative of an environment of the host vehicle, wherein the camera is positioned at a first location relative to the host vehicle; receive point cloud information from a LIDAR system onboard the host vehicle, wherein the LIDAR system is positioned at a second location relative to the host vehicle; analyze the at least one captured image and the received point cloud information to detect one or more objects in the shared field of view region; determine whether a vantage point difference between the first location of the camera and the second location of the LIDAR system accounts for the one or more detected objects being represented in only one of the at least one captured image or the received point cloud information.