Big Cell Grid Mapping for Real-Time Indoor Robot Positioning
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Solution Overview
Problem
Conventional methods for estimating the posture of a moving object, such as GPS and grid maps, face challenges in indoor use due to high error rates and memory requirements, while feature maps struggle with real-time calculations and accessibility issues.
Innovation Solution
A big cell grid map method that divides the map into finite cells, allowing for efficient storage and access of map feature point information, enabling accurate and fast posture estimation by extracting and matching local and global map feature points using sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a grid map is used to represent the environment, then the moving object can recognize the environment and perform real-time calculations, but a great amount of memory is required to store the information
Solution Approach 1:
The patent divides the map into a plurality of finite cells, creating a segmented representation where each cell contains only the map feature point information relevant to its position. This segmentation allows the system to access and process only the necessary portion of the map at any given time, reducing memory requirements while maintaining real-time calculation capability.
Solution Approach 2:
The patent implements local quality by storing map feature point information specifically associated with each cell's position rather than maintaining a complete detailed map in memory. Each cell contains localized information about map features at its corresponding position, allowing efficient storage and access of only the relevant environmental data needed for current positioning tasks.
2Measurement precision
If the cell size is kept small to express the environment in sufficient detail, then the environmental representation accuracy is improved, but the amount of data required for calculation increases fundamentally
Solution Approach 1:
The patent extracts only the essential map feature point information needed for positioning and stores it in the corresponding cells. Rather than storing complete detailed environmental data for every small cell, the system extracts and stores only the critical feature points that are necessary for accurate positioning, reducing the fundamental data amount while maintaining representation accuracy.
Solution Approach 2:
The patent applies partial action by loading and processing only the map feature point information corresponding to the current cell and nearby cells, rather than processing the entire map data. This allows the system to work with small cell sizes for accurate representation while only handling the partial data necessary for current positioning calculations.
3Quantity of substance
If a feature map is used to compress and store only necessary information, then memory requirements are reduced and calculation is simplified, but elements cannot be accessed on the basis of position information
Solution Approach 1:
The patent creates a hybrid data structure that serves multiple functions: it maintains the compressed storage efficiency of feature maps while adding position-based access capability through the cell grid structure. Each cell in the grid simultaneously serves as a spatial container and an access key, allowing the system to efficiently retrieve map feature point information based on position while maintaining reduced memory requirements.
4Device complexity
If GPS is used for self-position estimation, then the system is simple to implement, but the error range is wide and it cannot be used indoors
Solution Approach 1:
The patent introduces a cell-based map feature point matching system as an intermediary between the simple GPS approach and the need for accurate indoor positioning. The system uses the cell grid structure to organize map features and matches sensor data against these organized features, providing accurate indoor positioning without requiring complex sensor fusion systems, thus maintaining relative simplicity while improving precision.
Data Source
AI summary
Embodiments are proposed, including: a method for estimating the posture of a moving object by using a big cell grid map; a recording medium in which a program for implementing the method is stored; and the computer program stored in the medium to implement the method. More particularly, there are provided the embodiments including: a method for estimating the posture of a moving object by using a big cell grid map, the method dividing a map into a plurality of finite cells, and estimating and correcting the posture of a moving robot in the map (the big cell grid map) where map feature point information about a map feature point corresponding to a position of a corresponding cell is matched for each cell; a recording medium where a program for implementing the method is stored, and the computer program stored in the medium to implement the method.