Neural Network Point-Cloud Localization for Ambiguous Industrial Spaces
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Solution Overview
Problem
Current localization methods in industrial metaverses face challenges in accurately determining the pose of sensors within environments, particularly in areas where repetitive patterns or varying lighting conditions lead to ambiguity and reduced accuracy.
Innovation Solution
The proposed solution involves a multi-modal sensor fusion approach using 3D point clouds generated from sequences of images, which are encoded and processed through hierarchical layers to calculate similarities and correlations, enabling robust and accurate transformation estimation between different coordinate systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional localization methods are used in areas with repetitive patterns or varying lighting conditions, then the system can operate in these environments, but localization accuracy deteriorates due to ambiguity
Solution Approach 1:
The patent segments the localization problem into multiple modalities (visual features, depth information, RF signals) and processes each through separate encoder layers. By dividing the feature extraction into distinct channels that are later fused, the system can overcome the limitations of any single modality in ambiguous environments.
Solution Approach 2:
The patent creates a composite feature representation by fusing multiple modalities (visual, depth, RF) into a unified localization solution. This composite approach combines the strengths of different sensor types to achieve robust localization where single-modality methods fail due to repetitive patterns or lighting variations.
2Reliability
If multi-modal sensor fusion is implemented to improve localization robustness, then accuracy in ambiguous areas improves, but device complexity increases
Solution Approach 1:
The neural network is segmented into distinct encoder layers for different modalities (visual encoder, depth encoder, RF encoder) followed by separate fusion stages. This modular segmentation allows each modality to be processed independently through specialized pathways, reducing the complexity of fusing all modalities simultaneously while maintaining robust multi-modal integration.
3Measurement precision
If hierarchical layers are used to process encoded maps and calculate similarities, then localization accuracy scales to large areas, but computational complexity increases
Solution Approach 1:
The patent implements a hierarchical processing structure that segments the computational task into multiple stages: initial encoding of raw data, followed by separate processing of different feature types, then progressive fusion and matching. This segmentation allows complex similarity calculations to be broken down into manageable steps that scale to large areas without overwhelming computational requirements.
Solution Approach 2:
The patent transforms the localization problem from direct 2D image space matching to a multi-dimensional feature space that includes visual features, depth information, and RF signal characteristics. By operating in this expanded dimensional space and using hierarchical processing, the system achieves accurate matching across large areas while managing computational complexity through structured dimensionality reduction at each processing stage.
Data Source
AI summary
Encoding a first 3D point cloud of a first coordinate system into a first encoded map comprising first feature center points and first feature vectors;encoding a second 3D point cloud of a second coordinate system into a second encoded map comprising second feature center points and second feature vectors;adapting the first input feature vectors based on the first input feature vectors and the second input feature vectors, to obtain a first joint map;adapting the second input feature vectors based on the first input feature vectors and the second input feature vectors to obtain a second joint map;checking whether a correlation condition is fulfilled;extracting the coordinates of the first joint feature center points and the second joint feature center points if the correlation condition is fulfilled;calculating a transformation between the first coordinate system and the second coordinate system based on the pairs of extracted coordinates.