Pattern-Projected Self-Localization in Texture-Less Environments
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Solution Overview
Problem
Existing SLAM techniques struggle to provide accurate self-localization and navigation for mobile platforms in environments with self-similar surfaces and texture-less regions, as these surfaces cannot be reliably aligned or textured for localization.
Innovation Solution
A system comprising multiple mobile platforms equipped with optical depth sensors and at least one platform configured to operate as a static platform projecting patterns onto the environment, allowing mobile platforms to detect patterns and determine their position and navigation instructions using optical depth sensors and processors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SLAM techniques are used for self-localization in unknown environments, then the mobile platform can build a 3D map and estimate its location, but the system fails to provide accurate localization in environments with self-similar surfaces and texture-less regions
Solution Approach 1:
The patent introduces a projector as an intermediary device that projects artificial patterns onto the environment. These projected patterns serve as a mediator between the mobile platform and the self-similar/texture-less surfaces, creating distinguishable features that enable accurate localization. The projector transforms the environment by adding artificial visual cues that the depth sensor can reliably detect and use for positioning.
Solution Approach 2:
The system changes the visual parameters of the environment by projecting patterns with specific characteristics (geometry, intensity, temporal variation) onto self-similar surfaces. This parameter change transforms indistinguishable surfaces into distinguishable regions, allowing the depth sensor to detect unique features for accurate self-localization even in previously problematic environments.
2Measurement precision
If a robot projects a pattern onto the surface to enable localization, then localization may be possible, but the pattern moves along with the robot making it impossible to determine its location
Solution Approach 1:
The system segments the role of pattern projection from the mobile platform by using a separate stationary projector. This segmentation allows the pattern to remain stable in the environment while the mobile platform moves independently. The projector and mobile platform are separated into distinct functional components, preventing the pattern from moving with the robot.
Solution Approach 2:
The stationary projector acts as an intermediary that creates a stable reference frame in the environment. By separating the pattern projection function from the mobile platform, the system establishes an independent reference system that does not move with the robot, enabling accurate location determination.
3Reliability
If 3D registration of depth data is used for localization, then the system can work with depth information, but it cannot offer adequate self-localization in environments with self-similar surfaces
Solution Approach 1:
The projector serves as an intermediary that enhances surface distinguishability by adding artificial patterns. These patterns create unique depth signatures that make self-similar surfaces distinguishable to the depth sensor, thereby improving self-localization reliability in environments where natural features are insufficient.
Solution Approach 2:
The system changes the depth parameters of self-similar surfaces by projecting patterns that create distinct depth variations. This transforms surfaces that were previously indistinguishable in depth space into distinguishable regions, enabling reliable 3D registration and self-localization.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate self-localization and navigation of mobile platforms in unknown environments with self-similar surfaces and texture-less regions by projecting patterns and using optical depth sensors for pattern detection and position determination.
Implementation Method 1
the at least one static platform is configured to project a pattern onto the unknown environment
Implementation Method 2
each of the plurality of mobile platforms is configured to detect the pattern or a part thereof by its respective optical depth sensor
Data Source
AI summary
A system configured to operate in an unknown, possibly texture-less environment, with possibly self-similar surfaces, and comprising a plurality of platforms configured to operate as mobile platforms, where each of these platforms comprises an optical depth sensor, and one platform operates as a static platform and comprising at least one optical projector. Upon operating the system, the static platform projects a pattern onto the environment, wherein each of the mobile platforms detects the pattern or a part thereof by its respective optical depth sensor while moving, and wherein information obtained by the optical depth sensors, is used to determine moving instructions for mobile platforms within that environment. Optionally, the system operates so that every time period another mobile platform from among the plurality of platforms, takes the role of operating as the static platform, while the preceding platform returns to operate as a mobile platform.