Indoor Robot Positioning With Dual 2D Height-Zone Maps
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Indoor positioning for mobile robots is challenging due to the inability to use GPS and requires costly and time-consuming infrastructure setup, with existing image processing methods degrading accuracy when robots move quickly.
Innovation Solution
Generating 2D maps from 3D sensing data to differentiate between static and dynamic obstacles, estimating the robot's posture, and planning a safe movement path without complex calculations, using LiDAR sensors and 2D mapping to separate obstacles effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image processing techniques are used for indoor positioning, then localization capability is achieved, but calculation complexity increases and accuracy degrades when robots move quickly
Solution Approach 1:
The patent segments the 3D interior space into multiple 2D maps based on different height zones (first area and second area). Each 2D map represents a specific vertical slice of the environment, allowing the system to process spatial information in smaller, more manageable units rather than handling the entire 3D space at once, thus reducing calculation complexity while maintaining positioning accuracy
Solution Approach 2:
The patent transforms 3D spatial sensing data into 2D maps by orthogonally projecting the three-dimensional interior area onto two-dimensional planes. This dimensionality reduction simplifies the data structure and reduces computational burden while preserving essential spatial relationships needed for accurate indoor positioning
2Measurement precision
If infrastructure devices such as landmarks or beacons are installed for positioning, then localization accuracy improves, but construction and management costs increase
Solution Approach 1:
The system uses the moving apparatus's own sensing capabilities to generate and update 2D maps of the environment in real-time. Instead of relying on pre-installed infrastructure, the robot performs self-localization by processing sensor data from its surroundings, eliminating the need for expensive landmark or beacon installation while maintaining positioning accuracy
Solution Approach 2:
The patent changes the parameter representation from physical infrastructure markers to digital spatial parameters derived from sensor data. By representing the environment as 2D maps with coordinate systems and spatial relationships, the system achieves accurate positioning through data processing rather than physical markers, reducing infrastructure costs
3Measurement precision
If 2D maps are generated from 3D sensing data to differentiate obstacles, then obstacle differentiation accuracy improves, but data processing time increases
Solution Approach 1:
The patent segments the 3D sensing data into multiple 2D maps representing different height zones. This segmentation allows the system to process spatial information in smaller, more manageable units rather than handling the entire 3D point cloud at once, reducing data processing time while maintaining obstacle differentiation accuracy through focused analysis of each 2D slice
Data Source
AI summary
An indoor positioning method and apparatuses for performing the same are provided. The method includes generating a three-dimensional (3D) interior area for an interior based on sensing data obtained by sensing the interior, generating a first map of the 3D interior area based on a first area including a first obstacle in the entire area of the 3D interior area, generating a second map of the 3D interior area based on a second area including the first obstacle and a second obstacle different from the first obstacle in the entire area, and estimating a posture of the moving apparatus and planning a movement path, based on the first map and the second map.