RGB Camera Spatial Mapping for Low-Complexity SLAM
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Solution Overview
Problem
Existing SLAM map construction methods rely on costly hardware such as ToF cameras and black-and-white positioning cameras, lacking versatility and universality, which hinders miniaturization and increases equipment cost.
Innovation Solution
A method for drawing a spatial map using a single RGB camera and sensors to determine depth values and relative position coordinates, transforming them into spatial coordinates, allowing for the construction of SLAM maps without expensive hardware, ensuring versatility and cost-effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ToF cameras and black-and-white positioning cameras are used for SLAM map construction, then measurement precision and reliability are improved, but device complexity and cost increase
Solution Approach 1:
The patent applies universality by using a single RGB camera to perform multiple functions: capturing images for feature extraction and estimating depth information through algorithms. This multi-functional approach eliminates the need for dedicated depth sensing hardware like ToF cameras, thereby reducing device complexity while maintaining measurement precision through computational methods
Solution Approach 2:
The patent substitutes mechanical/optical depth sensing systems (ToF cameras, laser rangefinders) with computational algorithms that process RGB image data. By replacing hardware-based depth measurement with software-based depth estimation using visual features and geometric relationships, the system achieves comparable measurement precision without the complexity of specialized sensors
2Reliability
If ToF cameras and specialized sensors are used for SLAM map construction, then reliability is improved, but equipment cost increases
Solution Approach 1:
The patent employs inexpensive RGB cameras and standard processors that are widely available and cost-effective, replacing expensive specialized sensors. The approach uses computational algorithms that can be implemented on general-purpose hardware, significantly reducing equipment cost while maintaining reliable SLAM map construction through robust feature tracking and depth estimation
Solution Approach 2:
The system uses the RGB camera's inherent capabilities to serve dual purposes: capturing visual information for scene understanding and generating depth information through algorithmic processing. This self-service approach eliminates the need for additional dedicated depth sensing hardware, reducing costs while maintaining reliability through the camera's own data
3Measurement precision
If sophisticated sensors and high-computing processors are used for SLAM map construction, then measurement precision is improved, but ease of manufacture and miniaturization are worsened
Solution Approach 1:
The patent uses a single RGB camera to perform multiple functions including feature detection, tracking, and depth estimation, eliminating the need for multiple specialized sensors. This multi-functional approach reduces the overall system size and simplifies manufacturing, making miniaturization easier while maintaining measurement precision through algorithmic processing
Solution Approach 2:
The patent replaces complex mechanical/optical depth sensing systems with computational algorithms that run on standard processors. This substitution reduces hardware complexity and size, facilitating easier manufacturing and miniaturization while achieving comparable spatial measurement precision through software-based methods
Data Source
AI summary
A method for drawing a spatial map that can include determining a depth value of a feature point in an image captured by camera equipment in a current position, the depth value being a depth value in a relative coordinate system established with the current position of the camera equipment as a coordinate origin, determining a relative position coordinate of the feature point in the relative coordinate system based on the depth value, determining a spatial position coordinate of the feature point in a spatial coordinate system based on the relative position coordinate, the spatial coordinate system being a spatial coordinate system established with a preset position of the camera equipment as a coordinate origin, and drawing the spatial map according to the spatial position coordinate of the feature point and a pixel value of the image captured by the camera equipment, based on the spatial coordinate system.