Autonomous Vehicle Positioning Using Lidar Map Matching
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately determining their initial position due to limitations in GPS accuracy, especially in urban environments, and require pre-arranged landmarks for landmark-based localization, which is not feasible in all scenarios.
Innovation Solution
A method that uses a combination of GPS centroid coordinates, Inertial Measurement Unit (IMU) data, and lidar reflection points to determine the accurate position of an autonomous vehicle by analyzing road boundaries and surrounding static infrastructure, without the need for pre-arranged landmarks, using an Electronic Control Unit (ECU) that processes this information to correct the initial position.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS or GNSS receivers are used for initial position localization, then the system can obtain location information, but the accuracy is degraded to nearly 7.8 meters in urban environments due to poor sky view, building obstructions, and multi-path reflections
Solution Approach 1:
The patent introduces static infrastructure (buildings, poles, etc.) as an intermediary reference system. Instead of relying solely on satellite signals that are blocked or reflected in urban environments, the system uses locally available static structures as mediators to establish accurate position references through lidar scanning and map matching, thereby overcoming GPS signal degradation.
Solution Approach 2:
The patent replaces the satellite-based electromagnetic positioning system (GPS/GNSS) with a ground-based lidar scanning and map matching system. This substitution uses optical scanning of static infrastructure combined with pre-stored map data to determine position, achieving centimeter-level accuracy without relying on satellite signal quality.
2Measurement precision
If RTK correction signal technique is used to achieve centimetre level accuracy, then position precision is improved, but the system complexity and requirement for additional correction signals increases
Solution Approach 1:
The patent enables the autonomous vehicle to self-determine its position by scanning static infrastructure with lidar and matching it against pre-stored maps. The system serves itself by using its own sensors and existing map data without requiring external correction signals or additional infrastructure, thereby achieving high accuracy while reducing system complexity.
Solution Approach 2:
The patent performs preliminary actions by pre-storing high-resolution maps of static infrastructure in the operational environment. These pre-captured maps serve as reference data that enable accurate position determination through matching, eliminating the need for real-time correction signals and reducing system complexity.
3Measurement precision
If landmark based localization is used, then position can be calculated using known landmarks, but this method requires a lot of pre-arrangement of landmarks in the environment which is not possible at all instances
Solution Approach 1:
The patent enables the system to use naturally occurring static infrastructure (buildings, poles, etc.) that already exists in the environment, rather than requiring pre-arranged artificial landmarks. The autonomous vehicle scans and identifies these existing structures and matches them against pre-stored maps, achieving accurate localization without any special environmental preparation.
Solution Approach 2:
The patent makes the localization system universal by using static infrastructure that is naturally present in most urban and operational environments. Unlike landmark-based methods that require specific pre-arranged features, this approach can utilize any prominent static structure, making it applicable across diverse environments without special preparation.
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 accurate initial position determination of autonomous vehicles without pre-arranged infrastructure, improving accuracy and functionality in areas with low GPS signal strength, and eliminating the need for special infrastructure.
Implementation Method 1
the autonomous vehicle depends primarily on location service provided by GPS or GNSS receivers
Implementation Method 2
identifying a plurality of lidar reflection reference points within the approximate distance and direction of the static infrastructure
Data Source
AI summary
The present disclosure discloses method and an Electronic Control Unit (ECU) (101) of autonomous vehicle for determining an accurate position. The ECU (101) determines centroid coordinate from Global Positioning System (GPS) points, relative to autonomous vehicle and identifies approximate location and orientation of vehicle on pre-generated map based on centroid coordinate and Inertial Measurement Unit (IMU) data. Distance and direction of surrounding static infrastructure is identified from location and orientation of autonomous vehicle based on road boundaries analysis and data associated with objects adjacent to autonomous vehicle. A plurality of lidar reflection reference points are identified within distance and direction of static infrastructure based on heading direction of autonomous vehicle. Position of lidar reflection reference points are detected from iteratively selected shift positions from centroid coordinate. Thereafter, ECU (101) corrects initial position of autonomous vehicle by adding centroid coordinate with selected shift position to determine accurate position of autonomous vehicle.