Multi-Modal Sensor Fusion for Real-Time Sensor Registration
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Solution Overview
Problem
Current multi-modal sensor data fusion techniques for navigation and situational awareness in vehicles face challenges in accurately registering data from sensors with different coordinate systems, leading to misalignment issues that affect decision-making and perception tasks.
Innovation Solution
The method employs a deep convolutional neural network to fuse multi-modal sensor data, detect regions of interest, and apply post-processing to identify misalignments, using supervised or unsupervised learning to adjust sensor actuation and improve alignment, thereby enhancing sensor registration and data fusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate vision and LiDAR feature extraction methods are used to identify common anchor points, then sensor registration can be performed, but misalignment issues persist affecting perception accuracy
Solution Approach 1:
The patent merges vision and LiDAR feature extraction into a unified deep convolutional neural network that processes multi-modal sensor data simultaneously. This combined approach extracts features from both sensor types through shared and modality-specific layers, enabling accurate registration by identifying corresponding features across modalities without relying on separate processing pipelines that struggle with misalignment.
2Device complexity
If traditional data fusion methods are used to integrate multi-modal sensor data, then computation is simpler, but misalignment between sensors with different coordinate systems cannot be corrected
Solution Approach 1:
The patent changes the parameter space by transforming sensor data into a unified feature representation through learned transformations in the neural network. The model learns optimal parameter mappings between different sensor coordinate systems, enabling precise alignment by adjusting feature space parameters rather than relying on simple spatial transformations.
3Measurement precision
If deep convolutional neural networks are applied to detect misalignments and perform registration, then sensor alignment precision is improved, but computational complexity increases
Solution Approach 1:
The patent segments the deep neural network into distinct functional modules: shared feature extraction layers that process common patterns, modality-specific layers that handle sensor-type particularities, and registration layers that compute alignment transformations. This segmentation allows the complex registration task to be divided into manageable processing stages, reducing overall system complexity while maintaining high precision.
Data Source
AI summary
A method includes fusing multi-modal sensor data from a plurality of sensors having different modalities. At least one region of interest is detected in the multi-modal sensor data. One or more patches of interest are detected in the multi-modal sensor data based on detecting the at least one region of interest. A model that uses a deep convolutional neural network is applied to the one or more patches of interest. Post-processing of a result of applying the model is performed to produce a post-processing result for the one or more patches of interest. A perception indication of the post-processing result is output.