Intersection LiDAR Nodes for GPS-Free Absolute HD Mapping
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Solution Overview
Problem
Conventional high-definition (HD) map generation techniques rely on GPS and Real-Time-Kinematics (RTK) for absolute accuracy, which is not feasible in situations where these systems are unavailable, limiting the ability to create absolutely accurate maps without them.
Innovation Solution
The use of fixed-position smart intersection (IX) nodes equipped with LiDAR systems, which provide sensory data to a processor to generate an optimized HD map by combining LiDAR and Inertial Measurement Unit (IMU) data from vehicles, allowing for absolute accuracy without GPS or RTK.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS and RTK are used for map generation, then absolute accuracy is improved, but the system becomes inoperable in environments where GPS and RTK are unavailable
Solution Approach 1:
The patent introduces fixed-position IX nodes as intermediary reference points with known absolute positions. These nodes serve as mediators between the relative SLAM mapping system and the absolute coordinate system, enabling the vehicle to achieve absolute accuracy through relative measurements against the IX nodes without requiring GPS or RTK signals.
Solution Approach 2:
The patent extracts the absolute positioning function from the GPS/RTK system and relocates it to the fixed-position IX nodes. By separating the absolute reference function from the mobile vehicle system, the solution enables absolute accuracy to be achieved through infrastructure-based references rather than satellite-based systems.
2Adaptability or versatility
If conventional SLAM techniques are used, then the system operates without GPS, but absolute accuracy cannot be achieved
Solution Approach 1:
The patent merges conventional SLAM techniques with fixed-position IX node references. The vehicle's SLAM system continues to operate independently without GPS, while the IX nodes provide absolute position anchors. This combination preserves the operational independence of SLAM while adding the capability to achieve absolute accuracy through the merged system.
3Measurement precision
If multiple fixed-position IX nodes are deployed, then absolute accuracy is improved, but system complexity and infrastructure requirements increase
Solution Approach 1:
The patent segments the absolute positioning function into discrete, distributed IX nodes placed at specific locations throughout the mapped environment. Rather than requiring a continuous or centralized positioning infrastructure, the system divides the positioning task across multiple independent nodes, each providing localized absolute reference points that collectively enable global absolute accuracy.
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 the creation of absolutely accurate HD maps by leveraging stationary IX nodes with known positions, reducing dependency on GPS and RTK, and enhancing both absolute and relative accuracy of the maps.
Implementation Method 1
The use of fixed-position smart intersection (IX) nodes equipped with LiDAR systems
Data Source
AI summary
A computer-implemented method for controlling a vehicle includes receiving, via a processor, from two or more IX control devices disposed at a two or more stationary positions having known latitudes longitudes and orientations, first sensory data identifying the position and dimensions of a feature in a mapped region. The processor generates a plurality of IX nodes based on the first sensory data received from the IX control devices, and receives LiDAR point cloud that includes LiDAR and other vehicle sensory device data such as Inertial Measurement Unit (IMU) data received from a Vehicle (AV) driving in the mapped region. The LiDAR point cloud includes a simultaneous localization and mapping (SLAM) map having second dimension information and second position information associated with the feature in the mapped region. The processor generates, without GPS and/or real-time kinematics information, an optimized High-Definition (HD) map having Absolute accuracy using batch optimization and map smoothing.