Vehicle Pose Localization Using 3D Reference Model Alignment
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Solution Overview
Problem
Existing vehicle localization systems face challenges in achieving centimeter-level accuracy, especially in environments where on-board sensors are unavailable or have limited fields of view, making it difficult to precisely determine the position and orientation of vehicles for autonomous driving.
Innovation Solution
A system that utilizes a combination of stationary infrastructure sensors and computer algorithms to determine the pose of a vehicle in a coordinate system, employing 3D reference models and closest point algorithms to align sensor data over time, enabling accurate localization and tracking of vehicles with centimeter-level precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If on-board sensors are used for vehicle localization, then the system can determine position and orientation, but the localization precision is insufficient to achieve centimeter-level accuracy
Solution Approach 1:
The patent introduces stationary infrastructure elements (such as cameras or LiDAR sensors mounted on fixed structures) as intermediaries to capture images or data of the vehicle. These infrastructure elements provide an external, stable reference frame that mediates between the vehicle and the localization system, enabling precise pose determination without relying solely on the vehicle's own sensors.
Solution Approach 2:
The system creates a 3D reference model (copy) of the vehicle based on images captured by stationary infrastructure elements. This digital copy is then used for alignment and pose estimation through closest point algorithms, allowing the system to determine vehicle pose with centimeter-level accuracy by comparing the reference model against actual sensor data.
2Adaptability or versatility
If on-board sensors are unavailable or have limited fields of view, then the vehicle cannot reliably determine its own pose, but the patent provides localization without requiring on-board sensors
Solution Approach 1:
Instead of having the vehicle use its own sensors to determine its pose (self-localization), the system inverts the approach by having stationary infrastructure elements capture images of the vehicle and use those images to determine the vehicle's pose. The vehicle becomes the passive object being observed rather than the active sensor platform, enabling localization without on-board sensors.
Solution Approach 2:
Stationary infrastructure elements serve as intermediaries that bridge the gap between the vehicle and the localization system. These elements capture vehicle images and provide data to the server, which then processes the images to determine pose information, enabling accurate localization even when the vehicle has no or limited on-board sensing capability.
3Measurement precision
If stationary infrastructure elements are deployed for precise localization, then centimeter-level accuracy can be achieved, but the system complexity increases
Solution Approach 1:
The stationary infrastructure elements serve multiple functions: they capture vehicle images for localization, can potentially serve other traffic management functions, and provide a shared reference framework for multiple vehicles. This multi-functionality justifies the infrastructure investment and reduces overall system complexity by consolidating capabilities into versatile components.
Solution Approach 2:
The system uses computational copying through 3D reference models and closest point algorithms to achieve precise localization without requiring complex physical measurement devices on each vehicle. The digital modeling and algorithmic alignment provide a software-based solution that reduces hardware complexity while maintaining centimeter-level accuracy.
Data Source
AI summary
A reference pose of an object in a coordinate system of a map of an area is determined. The reference pose is based on a three-dimensional (3D) reference model representing the object. A first pose of the object is determined as the object moves with respect to the coordinate system. The first pose is determined based on the reference pose and sensor data collected by the sensor at a first time. A second pose of the object is determined as the object continues to move with respect to the coordinate system. The second pose is determined based on the reference pose, the first pose, and sensor data collected by the sensor at a second time consecutive to the first time.