Robot Navigation Using RGB Spatial Occupancy Models
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Solution Overview
Problem
Conventional methods for controlling robots in environments rely on depth data, which can be inaccurate and have low resolution, particularly for transparent, reflective, or occluded surfaces, limiting the creation of accurate 3D reconstructions and effective obstacle avoidance.
Innovation Solution
The use of RGB images to generate a representation of spatial occupancy within an environment, employing techniques such as neural radiance fields and signed distance functions, allows for more accurate and higher-resolution control of robots by determining robot actions and movements based on these representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If depth data is used to create 3D reconstruction for robot control, then the system can operate with depth sensing capability, but the accuracy and resolution of the environment representation deteriorates
Solution Approach 1:
The patent replaces depth-based mechanical sensing systems with vision-based RGB image processing systems. Instead of relying on depth cameras to directly measure spatial occupancy, the system uses RGB images processed through neural radiance fields to infer 3D environmental structure, thereby achieving higher accuracy for transparent and reflective surfaces
Solution Approach 2:
The patent changes the fundamental parameter used for environment representation from depth values to RGB color information. By using color-based neural radiance fields instead of depth-based reconstructions, the system achieves improved measurement precision for challenging surfaces while maintaining reliable environment representation
2Loss of information
If conventional depth cameras are used to acquire environment data, then the system can obtain depth information, but the resolution and accuracy of complex geometries deteriorates
Solution Approach 1:
The patent creates a virtual copy of the environment using neural radiance fields trained on RGB images. This virtual representation captures complex geometries and fine details with higher precision than direct depth sensing, while preserving all necessary spatial information for robot control
Solution Approach 2:
The patent transitions from 2D depth maps to 3D volumetric representations through neural radiance fields. By rendering depth information from multiple viewpoints synthesized from RGB images, the system achieves superior geometry detail resolution while maintaining complete depth information availability
3Measurement precision
If RGB images are used instead of depth data for spatial occupancy representation, then the accuracy and resolution of environment modeling improves, but the complexity of processing and generating 3D representations increases
Solution Approach 1:
The patent performs preliminary training of neural radiance fields offline using collected RGB images. This pre-processing step creates ready-to-use 3D environment models that can be quickly queried during robot operation, thereby reducing real-time processing complexity while maintaining high spatial occupancy accuracy
Data Source
AI summary
One embodiment of a method for controlling a robot includes generating a representation of spatial occupancy within an environment based on a plurality of red, green, blue (RGB) images of the environment, determining one or more actions for the robot based on the representation of spatial occupancy and a goal, and causing the robot to perform at least a portion of a movement based on the one or more actions.