Vision-Based AI Occupancy Modeling for 3D Voxel Detection
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Solution Overview
Problem
Existing autonomous navigation systems struggle to accurately predict the occupancy status of surrounding environments, which is crucial for safe and reliable operation of autonomous vehicles and robots.
Innovation Solution
A trained artificial intelligence model processes image data from cameras to predict the occupancy status of voxels in the surroundings, generating a dataset and graphical indicators of occupied or unoccupied spaces, which can be used for navigation and collision avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor-based occupancy detection is used, then the system can detect objects in the environment, but the measurement precision and reliability of occupancy status prediction are insufficient
Solution Approach 1:
The patent replaces traditional mechanical sensor-based occupancy detection systems with an artificial intelligence model that processes image data. The AI model analyzes visual information from cameras to predict occupancy status of voxels in the environment, substituting physical sensor arrays with intelligent image processing to achieve higher prediction accuracy and reliability for autonomous navigation
Solution Approach 2:
The patent transitions from 2D image data to 3D occupancy prediction by dividing the environment into volumetric voxels. The AI model processes 2D camera images and generates 3D occupancy status predictions for multiple voxels, adding a spatial dimension to the detection output to improve both precision and reliability of occupancy determination
2Measurement precision
If multiple sensors are deployed to improve detection accuracy, then occupancy detection capability is enhanced, but the device complexity increases
Solution Approach 1:
The patent replaces complex multi-sensor hardware systems with a software-based AI model that processes image data from cameras. This substitution maintains or improves occupancy detection accuracy while significantly reducing device complexity by eliminating the need for multiple physical sensors and their associated processing systems
Solution Approach 2:
The AI model serves multiple functions: it processes image data from cameras, predicts occupancy status of multiple voxels simultaneously, and provides navigation guidance. This multi-functionality consolidates what would traditionally require multiple specialized sensors into a single versatile system, reducing overall device complexity
Data Source
AI summary
Disclosed herein are methods and systems for using artificial intelligence modeling techniques to train and execute an artificial intelligence model to analyze camera feed received from an ego to generate an occupancy data indicating whether different voxels within the ego's surroundings are occupied by an object having mass. A method comprises inputting, using a camera of an ego object, image data of a space around the ego object into an artificial intelligence model; predicting, by executing the artificial intelligence model, an occupancy attribute of a plurality of voxels; and generating a dataset based on the plurality of voxels and their corresponding occupancy attribute.