Robot Floor Plan Mapping with Pixel-Depth Data Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous robots face challenges in accurately perceiving and navigating complex environments due to limitations in spatial mapping and object recognition, particularly in combining pixel characteristics and depth data from various positions and fields of view.
Innovation Solution
The use of a robot equipped with sensors that capture both pixel characteristics and depth data, utilizing structured light emissions and depth sensors, with a processor aligning this data to create a precise spatial model of the environment, enabling improved navigation and task planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the robot uses multiple sensors to capture pixel characteristics and depth data from different positions and fields of view, then the accuracy of spatial model perception is improved, but the device complexity increases
Solution Approach 1:
The patent combines pixel characteristic data and depth data from multiple sensors positioned at different locations with varying fields of view. The processor integrates these heterogeneous data types to construct a unified spatial model, merging information from diverse sources to achieve comprehensive environmental perception while managing system complexity through integrated processing.
Solution Approach 2:
The patent transitions from two-dimensional pixel data to three-dimensional spatial modeling by incorporating depth information from structured light emissions and depth sensors. This dimensional enhancement allows the robot to perceive environmental features in 3D space, improving spatial model accuracy by adding the depth dimension to traditional 2D image data.
2Loss of information
If the robot captures data from multiple positions and fields of view to improve spatial perception, then the completeness of environmental mapping is improved, but the time required for data capture and processing increases
Solution Approach 1:
The patent performs preliminary alignment and integration of spatial data during the data capture phase. The processor begins aligning data from different positions and fields of view as data is being collected, rather than waiting for complete data acquisition. This preliminary processing reduces the overall time required for spatial model construction by preparing data for integration in advance.
Solution Approach 2:
The patent implements continuous data capture and processing operations. The robot continuously collects pixel characteristics and depth data from multiple sensors while simultaneously processing and integrating this information into the spatial model. This continuous operation eliminates idle time between data collection and processing, maintaining productive action throughout the mapping process.
3Reliability
If the robot integrates multiple data types including pixel characteristics and depth data, then the reliability of spatial model perception is improved, but the computational requirements and processing complexity increase
Solution Approach 1:
The patent replaces complex mechanical sensor arrangements with computational methods for data integration. Instead of using multiple physically complex sensor systems, the invention uses a processor to computationally align and integrate data from existing sensors. This substitution of mechanical complexity with computational processing maintains reliability while managing system complexity through software-based solutions.
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
This approach enhances the robot's ability to create accurate spatial models, leading to more efficient navigation and task completion in diverse environments by integrating multiple data types effectively.
Implementation Method 1
the first data and the second data are captured by at least one of: a same sensor; a same sensor in combination with structured light emissions
Implementation Method 2
second data indicative of depth to objects in the environment; the first data and the second data are captured by at least one of: a same sensor in combination with structured light emissions; and at least one depth sensor
Data Source
AI summary
A robot for perceiving a spatial representation of an environment, including: an actuator, at least one sensor, a processor, and memory storing instructions that when executed by the processor effectuates operations including: capturing a plurality of data by the at least one sensor of the robot, wherein: the plurality of data comprises first data comprising pixel characteristics indicative of features of the environment and second data indicative of depth to objects in the environment; the plurality of data is captured from different positions within the environment through which the robot moves, the plurality of data corresponding with respective positions from which the plurality of data was captured; and the plurality of data captured from different respective positions within the environment corresponds to respective fields of view; and aligning the plurality of data as it is captured to more accurately perceive the spatial representation of the environment.