Stereo Camera Ego-Motion Estimation for Precise Vehicle Position Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing odometry units in vehicles have poor resolution, making it difficult to accurately detect changes in vehicle position, which is particularly disadvantageous for autonomous driving functions.
Innovation Solution
A method using a stereo camera system with at least two cameras and an artificial neural network to process image information, generating stereo images with distance information and estimating ego-motion by analyzing temporal changes in image sequences, providing high-accuracy information on translational and rotational motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional odometry units are used with wheel rotation sensors, GPS sensors, and yaw rate sensors, then the system structure is simple and easy to implement, but the measurement precision of vehicle position changes is poor
Solution Approach 1:
The patent replaces traditional mechanical odometry sensors (wheel rotation sensors, GPS, yaw rate sensors) with an optical-based vision system. The stereo camera system captures image sequences, and an artificial neural network processes these images to determine ego-motion, substituting mechanical measurement mechanisms with optical and computational methods to achieve higher precision.
Solution Approach 2:
The patent introduces an artificial neural network as an intermediary between the stereo camera system and the ego-motion determination. The neural network processes the image sequences and extracts motion information, acting as a mediator that transforms visual data into precise position change measurements, thereby achieving high accuracy without direct mechanical sensing.
2Measurement precision
If stereo camera system with artificial neural network is used, then the measurement precision of ego-motion is significantly improved, but the device complexity and computational requirements increase
Solution Approach 1:
The patent employs pre-trained artificial neural networks that have been previously trained on large datasets. This preliminary training action allows the system to perform high-precision ego-motion determination during operation without requiring complex real-time training computations, thus reducing operational complexity while maintaining high measurement precision.
Solution Approach 2:
The patent divides the complex task of ego-motion determination into separate functional modules: the stereo camera system captures image sequences, the artificial neural network processes these sequences to extract motion information, and the system outputs ego-motion parameters. This segmentation allows each component to be optimized independently, managing overall system complexity.
Data Source
AI summary
A method for determining information about the ego-motion of a vehicle, wherein the vehicle comprises a stereo camera system with at least two cameras for capturing stereo images of the area surrounding the vehicle and an artificial neural network for processing the image information provided by the stereo camera system, wherein the stereo camera system captures image sequences which contain a plurality of image information at different points in time by means of the at least two cameras during the movement of the vehicle, wherein the artificial neural network receives the image information and, based thereon, generates stereo images with distance information, and the artificial neural network provides information regarding the ego-motion of the vehicle at an output interface on the basis of the image information of the stereo camera system.