Neural Network Image Registration for Vehicle Localization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing localization systems in autonomous and semi-autonomous driving rely heavily on GPS, which can be inaccurate or unavailable due to malfunctions, leading to potential collisions and navigation errors.
Innovation Solution
A computer-implemented method and system that uses a processing system with sensors to generate localization estimates and update data by optimizing the registration of map images with visualization images from various sensors, providing accurate localization output data in real-time, even in the absence of reliable GPS data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS data is used for localization, then the localization system is simple to implement, but the localization accuracy deteriorates when GPS is malfunctioning or unavailable
Solution Approach 1:
The patent combines multiple localization approaches (GPS-based global localization and sensor-based local localization) into a unified system. The processing system integrates GPS data with sensor data from cameras and other sensors, merging global position estimates with local visual features to achieve reliable localization that works both when GPS is available and when it is not.
Solution Approach 2:
The processing system is designed to perform multiple localization functions using a single integrated system. It can switch between GPS-based localization and sensor-based visual localization, making the system universal and adaptable to different operating conditions (GPS available, GPS unavailable, partial GPS failure).
2Measurement precision
If multiple sensors are used to generate localization data, then the localization accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent segments the localization task into distinct functional components: global localization (GPS-based) and local localization (sensor-based). The sensor system is divided into specialized sensors (cameras for visual features, other sensors for additional data), each performing specific functions. This segmentation allows the system to achieve high precision while managing complexity through modular design.
Solution Approach 2:
The processing system acts as an intermediary that integrates data from multiple sensors. It receives data from GPS, cameras, and other sensors, then processes and combines this information to generate unified localization output. This intermediary role manages the complexity of multiple sensors by providing a centralized processing layer that coordinates their outputs.
Data Source
AI summary
A system and method for localizing an entity includes a processing system with at least one processing device. The processing system is configured to obtain sensor data from a sensor system that includes at least a first set of sensors and a second set of sensors. The processing system is configured to produce a map image with a map region that is selected and aligned based on a localization estimate. The localization estimate is based on sensor data from the first set of sensors. The processing system is configured to extract sets of localization features from the sensor data of the second set of sensors. The processing system is configured to generate visualization images in which each visualization image includes a respective set of localization features. The processing system is configured to generate localization output data for the vehicle in real-time by optimizing an image registration of the map image relative to the visualization images.