Neural Network 3D Environment Parsing for Robotics
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Solution Overview
Problem
Existing machine vision technologies face challenges in efficiently processing three-dimensional environmental data to detect objects in real-time, especially with limited computational resources and time constraints, which is crucial for robotics and autonomous systems.
Innovation Solution
The use of neural networks to parse and evaluate three-dimensional environments, where two neural networks are trained to extract 3D information and object boundaries from camera data and optical flow, enabling efficient detection of objects even with limited resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine vision processing methods are used to detect objects in three-dimensional environments, then measurement precision and reliability can be maintained, but processing time increases and productivity decreases
Solution Approach 1:
The patent segments the three-dimensional environment into discrete voxels (volume elements) that can be independently processed. Each voxel represents a small spatial unit that can be evaluated separately by the neural network, allowing parallel processing of multiple spatial locations simultaneously. This segmentation enables the system to handle complex three-dimensional scenes efficiently while maintaining detection accuracy.
Solution Approach 2:
The patent transforms the processing approach by introducing a volumetric dimension through voxel-based representation. Instead of processing continuous three-dimensional space or complex point clouds, the system discretizes space into a grid of voxels, converting the problem into a structured volumetric array that can be processed more efficiently by neural networks while preserving spatial relationships.
2Reliability
If comprehensive processing of three-dimensional environmental data is performed to ensure reliable object detection, then measurement precision improves, but computational resource consumption increases
Solution Approach 1:
The patent applies partial action by having the neural network process only the most relevant voxels or regions of interest within the three-dimensional environment, rather than uniformly processing every voxel with equal computational intensity. The system can focus computational resources on areas where objects are more likely to be present or where detection is more critical, reducing overall computational load while maintaining reliable detection.
Solution Approach 2:
The patent uses virtual generated scenes as training data, creating synthetic copies of real-world environments. By training the neural network on these virtual copies populated with known objects and ground truth data, the system learns to reliably detect objects in real environments without requiring exhaustive processing of every real-scene data point during operation.
3Productivity
If real-time processing is implemented to meet time constraints in dynamic environments, then productivity improves, but measurement precision and reliability may deteriorate
Solution Approach 1:
The patent performs preliminary action by pre-training the neural network on extensive virtual generated scenes before deployment. This pre-training phase allows the network to learn robust object detection patterns and spatial relationships in advance. During real-time operation, the pre-trained network can quickly process incoming three-dimensional data with high accuracy without requiring extensive computation during the critical real-time detection phase.
Data Source
AI summary
Machine vision apparatus, methods and articles advantageously employ neural networks to parse or evaluate a three-dimensional environment which may, for example, be useful in robotics. Such may advantageously allow detection of objects (e.g., targets, obstacles, or even portions of the robot itself or neighboring robots) in a three-dimensional environment using limited physical computational resources, limited processing time, and/or with a high level of accuracy. Detection may include representation of a volume occupied by an object in the three-dimensional environment and/or a pose (i.e., position and orientation) of the object in the three-dimensional environment. Such may, for example, allow a robot to engage or otherwise interact with one or more target objects while avoiding obstacles in the three-dimensional environment, for instance while the robot operates autonomously and/or in real-time in the three-dimensional environment.