Single-Image Pose Estimation for Autonomous Vehicle Localization
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Solution Overview
Problem
Autonomous vehicles face high computational complexity and errors in localization due to the need to process multiple images simultaneously, and issues with object visibility affecting localization accuracy, especially in conditions like rain or snow.
Innovation Solution
A machine learning model processes single images from sensors to determine vehicle pose changes based on blurring, using trained neural networks to output pose changes, which are then aggregated to determine global position, reducing computational complexity and improving localization accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple images are processed simultaneously for localization, then localization accuracy can be improved through comparison, but computational complexity and processing requirements increase significantly
Solution Approach 1:
The patent extracts only the essential information needed for localization from images by using a machine learning model that processes single images to determine pose changes. Instead of comparing multiple full images, the system extracts pose change information directly from individual images, significantly reducing computational complexity while maintaining localization accuracy.
Solution Approach 2:
The patent replaces the mechanical/image processing system with a machine learning-based system. Instead of using traditional computer vision algorithms that require multiple images and complex processing, the system uses a trained neural network that can determine pose changes from single images, substituting computational image processing with intelligent pattern recognition.
2Reliability
If multiple images are used for localization, then more data is available for accurate positioning, but the system requires high processing speed and memory capacity
Solution Approach 1:
The system extracts only the necessary pose change information from single images using a machine learning model, avoiding the need to process and store multiple complete images. This extraction approach maintains localization reliability while significantly reducing memory capacity requirements and processing energy consumption.
3Measurement precision
If the camera is blocked by objects or weather conditions, then localization using multiple images fails, but the system needs to maintain accurate positioning
Solution Approach 1:
The patent replaces traditional multi-image computer vision localization with a machine learning-based single-image pose estimation system. The neural network is trained to extract pose information even from degraded or partially obscured images, making the system more robust to sensor blockage from weather conditions or objects while maintaining localization accuracy.
Data Source
AI summary
A vehicle comprises a sensor configured to capture images and one or more processors. The one or more processors can be configured to receive a single image from the sensor, the single image captured by the sensor as the autonomous vehicle was moving; execute a machine learning model using the single image as input to generate a change in pose of the autonomous vehicle, the machine learning model trained to output changes in pose of autonomous vehicles based on blurring in individual images; determine a global position of the autonomous vehicle based on the generated change in pose of the autonomous vehicle; and transmit the global position to an autonomous vehicle controller configured to control the autonomous vehicle.