Hierarchical 3D Map Building for Moving Robots Using TOF Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for building 3D maps of ambient environments using Time of Flight (TOF) cameras in moving robots face accuracy issues as the travel distance increases, due to errors in location information and high calculation times, especially with the sequential map building method using the Iterative Closest Point (ICP) algorithm, which can converge to local minima and incorrectly match 3D point cloud data.
Innovation Solution
A hierarchical map building method that accumulates 3D distance information based on a coordinate system and uses the ICP algorithm only for matching maps of equal size levels, stored in a database, to construct a 3D map for a set space, reducing calculation time and errors by iteratively increasing the size level of the map and combining localization results with ICP algorithm outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the occupancy voxel map building method is used, then the map can be built in real-time using TOF camera data, but the map accuracy decreases as the robot's travel distance increases due to location information errors
Solution Approach 1:
The patent segments the map building process into multiple levels based on robot travel distance. The space is divided into multiple regions, and maps are constructed hierarchically from local to global scales. This allows real-time processing at each level while maintaining overall accuracy through progressive refinement.
Solution Approach 2:
The patent introduces a hierarchical dimension to the map building process, transitioning from a single-level occupancy grid to a multi-level structure. This adds a temporal and spatial hierarchy, allowing the system to process data at different scales simultaneously, thus maintaining both real-time performance and accuracy.
2Measurement precision
If the sequential map building method using ICP algorithm is used, then the map accuracy is improved by correctly matching 3D point cloud data, but the calculation time increases significantly
Solution Approach 1:
The patent segments the point cloud matching process into multiple levels. At each level, only corresponding regions are matched using the ICP algorithm, rather than processing the entire point cloud. This reduces calculation time while maintaining accuracy through hierarchical refinement.
Solution Approach 2:
The patent performs preliminary processing by dividing the space into multiple regions and pre-processing point cloud data at each level before applying the ICP algorithm. This preliminary organization reduces the computational burden during the actual matching process, decreasing calculation time while preserving accuracy.
3Area of stationary object
If the sequential map building method is used to cover larger spaces, then the map coverage area increases, but the calculation time increases and error accumulation occurs
Solution Approach 1:
The patent divides the large space into multiple smaller regions or levels. Each region is processed independently and then integrated into the global map. This segmentation allows parallel processing and reduces the computational time required for large-area mapping while preventing error accumulation through localized processing.
Solution Approach 2:
The patent adds a hierarchical dimension to map coverage, organizing the large space into a multi-level structure. This allows the system to manage and process large areas efficiently by working at appropriate scales, reducing calculation time and preventing error propagation across the entire map.
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 accuracy and efficiency of map building by reducing processing time and minimizing incorrect correlations between 3D point cloud data, allowing for the construction of more accurate and larger-scale maps compared to conventional methods.
Implementation Method 1
The TOF camera is a device that acquires 3D distance information using a TOF method which measures the time that it takes for infrared light emitted from a light emitting unit to return to a light receiving unit after being reflected from an object.
Data Source
AI summary
A moving robot and a method to build a map for the same, wherein a 3D map for an ambient environment of the moving robot may be built using a Time of Flight (TOF) camera that may acquire 3D distance information in real time. The method acquires 3D distance information of an object present in a path along which the moving robot moves, accumulates the acquired 3D distance information to construct a map of a specific level and stores the map in a database, and then hierarchically matches maps stored in the database to build a 3D map for a set space. This method may quickly and accurately build a 3D map for an ambient environment of the moving robot.