Neural Network Scene Representation for SLAM Mapping
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Solution Overview
Problem
Current SLAM systems face challenges in real-time operation due to computational demands, accuracy limitations, and sensitivity to lighting and texture conditions, leading to poor environmental understanding and navigation issues.
Innovation Solution
The use of a neural network scene representation that maps point locations in 3D space to higher dimensionality feature tensors, enabling a differentiable plug-in component for SLAM systems, allowing end-to-end learning and optimization of mapping functions without explicit storage of 3D point data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional point cloud representations are used for SLAM mapping, then the system can store explicit 3D spatial data, but the computational requirements and storage needs increase significantly
Solution Approach 1:
The patent extracts only the essential information needed for navigation and mapping from the full 3D point cloud data. Instead of storing all spatial points, it extracts and stores only the necessary feature descriptors and spatial relationships in a compressed neural network representation, significantly reducing storage requirements while maintaining navigation accuracy.
Solution Approach 2:
The patent transforms the representation from explicit 3D point cloud parameters to neural network parameter space. The neural network learns to map 3D positions to high-dimensional feature vectors, changing the parameter space from raw spatial coordinates to learned features, which reduces storage and computational complexity.
2Measurement precision
If dense mapping techniques are used to improve environmental understanding, then mapping accuracy improves, but real-time processing capability deteriorates
Solution Approach 1:
The patent changes the computational approach by using a neural network that processes 3D positions through learned feature transformations. This parameter change enables the system to achieve dense mapping accuracy while maintaining real-time performance, as the neural network efficiently computes high-dimensional feature vectors without the heavy computational burden of traditional dense point cloud processing.
3Reliability
If traditional SLAM systems process all pixels for dense mapping, then mapping completeness improves, but computational complexity increases
Solution Approach 1:
The patent transforms the computational complexity by using a neural network that processes spatial information through efficient parameter transformations. The network maps 3D positions to high-dimensional feature vectors through learned operations, achieving complete environmental mapping with reduced computational complexity compared to traditional pixel-by-pixel dense mapping approaches.
Data Source
AI summary
Certain examples described herein relate to a mapping system. An example mapping system has a differentiable mapping engine to receive image data comprising a sequence of images captured using one or more camera devices of an object as it navigates an environment and a neural network scene representation comprising a neural network architecture trained to map input coordinate tensors indicating at least a point location in three-dimensional space to scene feature tensors having a dimensionality greater than the input tensors. The neural network scene representation is communicatively coupled to the differentiable mapping engine and the differentiable mapping engine is configured to use the neural network scene representation as a mapping of the environment during operation of the differentiable mapping engine.