3D Viewpoint Simulation for Autonomous Navigation Training
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Solution Overview
Problem
Mobile systems face challenges in accurately navigating their surroundings due to the limited amount of real-world input available, especially in varying conditions, as input from one location cannot be replicated in different environments like snowy or blizzard conditions.
Innovation Solution
A computing system generates a virtual environment with ground-truth heuristics to simulate sensor data, creating multiple instances of physical environments and viewpoints, allowing for extensive training data that exceeds real-world limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If real-world input is collected for training mobile systems to navigate, then the system can learn from actual environmental conditions, but the volume of input is insufficient to cover all possible conditions and locations
Solution Approach 1:
The patent creates virtual copies of physical environments through 3D modeling and rendering. These virtual environments replicate real-world locations, roadways, and environmental features, allowing unlimited generation of training data without leaving the physical world. The system generates synthetic images and sensor data from these virtual copies to expand training volume.
Solution Approach 2:
The system modifies environmental parameters in the virtual models to create variations of the same location under different conditions. By changing parameters such as weather, lighting, time of day, and environmental features while maintaining the underlying 3D structure, the system generates diverse training data that covers all possible conditions for a given location.
2Measurement precision
If the system collects input data from one physical location, then it can obtain real environmental information, but it cannot obtain input for that same location under different conditions (e.g., snowy vs. tropical)
Solution Approach 1:
The virtual environment system allows dynamic modification of environmental conditions while maintaining the same physical location model. The 3D model can be rendered under different weather conditions, lighting scenarios, and environmental states, enabling the system to study the same location under multiple conditions simultaneously.
Solution Approach 2:
The system creates multiple virtual copies of the same physical location, each representing different environmental conditions. These copies share the same 3D structural model but differ in atmospheric, weather, and environmental parameters, allowing accurate representation of various conditions without requiring multiple physical visits.
3Quantity of substance
If the system generates multiple virtual instances of a physical environment, then it can create extensive training data, but the process requires complex 3D modeling and rendering computations
Solution Approach 1:
The system divides the physical environment into discrete 3D models of key locations and features. By segmenting the environment into manageable 3D representations of roadways, intersections, and surrounding areas, the system can efficiently render multiple virtual instances without requiring complete reconstruction of entire landscapes.
Solution Approach 2:
The 3D modeling and rendering infrastructure serves multiple functions: creating virtual environments, generating training data, simulating sensor perspectives, and producing diverse environmental variations. This multi-functional approach consolidates computational requirements into a single system rather than requiring separate specialized systems.
Data Source
AI summary
Aspects of this technical solution can generate, according to one or more first environment metrics, a three-dimensional (3D) model including a first surface corresponding to one or more physical ways through a physical environment, the one or more first environment metrics indicative of boundaries of the one or more physical ways, generate, according to one or more second environment metrics, one or more geometric two-dimensional (2D) objects on the first surface, the second environment metrics indicative of the one or more physical ways, identify, according to one or more viewpoint metrics indicative of cameras of a physical object configured to move along the one or more physical ways, one or more viewpoints oriented to capture corresponding portions of the 3D model, and render, from the one or more corresponding portions of the 3D model, one or more 2D images each corresponding to respective ones of the viewpoints.