Robotic Navigation With 3D Gaussian Splatting Semantic Maps
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Solution Overview
Problem
Existing three-dimensional exploration and navigation systems struggle to integrate semantic understanding with efficient spatial representations, leading to suboptimal exploration paths and resource inefficiencies in complex environments, particularly in robotics and virtual reality applications.
Innovation Solution
A system and method utilizing three-dimensional Gaussian splatting (3DGS) to create a continuous, explicit three-dimensional map that encodes both geometric and semantic information, enabling real-time rendering and navigation by converting discrete visual data into a dense cloud of Gaussian ellipsoids with embedded semantic information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If discrete methods like NeRFs and VLFMs are used to represent three-dimensional scenes, then semantic information can be encoded, but computational efficiency and real-time rendering capability deteriorate
Solution Approach 1:
The patent transforms the discrete parameter-based representation of NeRFs into a continuous spatial representation using Gaussian splatting. By changing from discrete sampling points to continuous Gaussian distributions in three-dimensional space, the system achieves both semantic information preservation and computational efficiency for real-time rendering
Solution Approach 2:
The patent replaces the complex neural network inference mechanism of NeRFs with a direct Gaussian splatting rendering approach. This substitution eliminates the need for repeated neural network forward passes during rendering, significantly improving computational efficiency while maintaining semantic representation capabilities
2Device complexity
If discrete visual data is used for navigation, then data processing is simpler, but navigation precision and path optimality deteriorate
Solution Approach 1:
The patent transitions from two-dimensional image data to a three-dimensional continuous spatial representation using Gaussian splatting. This dimensional transformation enables precise depth estimation and spatial understanding, improving navigation precision while maintaining computational tractability through the differentiable nature of Gaussian operations
3Adaptability or versatility
If semantic information is integrated into three-dimensional maps, then exploration decision-making improves, but computational overhead and processing time increase
Solution Approach 1:
The patent performs semantic segmentation and Gaussian splatting in a preliminary offline processing stage, creating a pre-computed continuous three-dimensional map with embedded semantic information. During online navigation and exploration, the system directly queries this pre-processed map, significantly reducing real-time processing time while maintaining advanced decision-making capabilities
Data Source
AI summary
A method of and system for navigation and manipulation for a robot can include obtaining, by at least one camera and at least one depth sensor, a first visual data set and translating the first visual data set into a continuous three-dimensional map. The three-dimensional map can include semantic information and geometric information. The method and system may further include receiving instruction data and converting the instruction data into at least one task for the robot within the continuous three-dimensional map.