Cross-Modal Sensor Data Alignment via Embedding Matching
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Solution Overview
Problem
Existing systems face challenges in accurately aligning cross-modal sensor data, such as images from camera sensors and point clouds from surveying sensors, which are essential for sensor calibration, object localization, and object detection, particularly in real-time applications like autonomous vehicles, where latency can be critical.
Innovation Solution
A cross-modal alignment system using visual embedding neural networks and shape embedding neural networks to generate region embeddings, which are then matched using a greedy nearest neighbor algorithm, allowing for rapid and accurate alignment of image and point cloud data, even in dynamic environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional alignment methods are used to align cross-modal sensor data, then alignment accuracy can be achieved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent transforms the alignment problem from direct spatial coordinate matching to embedding space matching. By projecting sensor data into learned embedding representations and computing similarities in this transformed parameter space, the system achieves accurate alignment with reduced computational complexity compared to traditional direct matching methods
Solution Approach 2:
The patent introduces embedding representations as an intermediary layer between the raw sensor data and the alignment process. These embeddings serve as a mediator that captures essential features while reducing the dimensionality and complexity of the direct alignment computation, enabling efficient cross-modal matching
2Measurement precision
If traditional alignment methods are used to align cross-modal sensor data, then alignment accuracy can be achieved, but processing speed decreases due to high computational requirements
Solution Approach 1:
The patent changes the computational parameters from operating on raw high-dimensional sensor data to operating on compressed embedding representations. This parameter transformation maintains alignment accuracy while dramatically improving processing speed by reducing the computational burden of similarity calculations
Solution Approach 2:
The patent segments the alignment process into distinct stages: embedding generation from sensor data, similarity computation in embedding space, and region pair identification. This segmentation allows for optimized computation at each stage, improving overall processing speed while maintaining accuracy
3Loss of time
If sensor data is processed in real-time for autonomous vehicle applications, then latency is reduced, but alignment accuracy may deteriorate due to computational constraints
Solution Approach 1:
The patent performs preliminary processing by generating embedding representations from sensor data before the alignment operation. These pre-computed embeddings can be stored and reused, enabling rapid alignment operations in real-time autonomous driving scenarios without sacrificing accuracy
Solution Approach 2:
The patent transforms the data representation into a more computationally efficient parameter space (embeddings) that maintains the essential information needed for accurate alignment while enabling faster processing suitable for real-time applications with strict latency requirements
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining an alignment between cross-modal sensor data. In one aspect, a method comprises: obtaining (i) an image that characterizes a visual appearance of an environment, and (ii) a point cloud comprising a collection of data points that characterizes a three-dimensional geometry of the environment; processing each of a plurality of regions of the image using a visual embedding neural network to generate a respective embedding of each of the image regions; processing each of a plurality of regions of the point cloud using a shape embedding neural network to generate a respective embedding of each of the point cloud regions; and identifying a plurality of region pairs using the embeddings of the image regions and the embeddings of the point cloud regions.