Stereo Camera Object Localization with Sparse Map Navigation
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating effectively due to the vast amount of data they need to process and store, particularly with traditional mapping technologies, which can limit their ability to accurately identify objects and navigate through environments.
Innovation Solution
A navigation system that utilizes multiple cameras to capture images from different angles, processes these images using trained models to determine object signatures, and combines the data to accurately locate objects within the vehicle's environment, employing a sparse map for efficient navigation without the need for extensive data storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mapping technology is used for autonomous vehicle navigation, then comprehensive environmental data is available, but data storage requirements and processing complexity increase significantly
Solution Approach 1:
The patent extracts only the essential navigational features from the environment (road edges, intersections, landmarks) to create a sparse map, removing unnecessary detailed data while retaining sufficient information for accurate navigation and object location determination
Solution Approach 2:
The patent segments the environmental data into discrete key features (road boundaries, intersections, landmarks) rather than storing continuous comprehensive map data, enabling efficient storage and processing while maintaining navigation reliability
2Measurement precision
If multiple camera images are processed to determine object location, then detection accuracy improves, but processing time and computational complexity increase
Solution Approach 1:
The patent uses trained machine learning models that have been pre-trained on large datasets to quickly infer object locations from multiple camera images, replacing complex real-time processing with efficient model-based prediction that maintains high accuracy
Solution Approach 2:
The patent performs preliminary processing by extracting key features from multiple camera images and using trained models to predict object locations before final determination, reducing the computational burden of real-time processing while maintaining accuracy
Data Source
AI summary
Systems and methods for navigating a host vehicle are disclosed. In one implementation at least one processor is programmed to receive a first signature encoding generated by a first trained model implemented by a first processor associated with a first camera; receive a second signature encoding generated by a second trained model implemented by a second processor associated with a second camera; input the first signature encoding and the second signature encoding into a third trained model, wherein the third trained model is configured to determine a location of an object represented in the first image and the second image; and receive an indicator of the location of the object determined by the third trained model.