Construction Robot Localization Using Semantic Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for localizing mobile construction robots on construction sites are not robust or precise, especially in environments with interfering objects, and often require manual interventions or inflexible BIM data-based solutions.
Innovation Solution
A method that captures environment data using sensors like LIDAR scanners, performs semantic segmentation to classify data into background and foreground, and updates a semantic model to improve localization accuracy, allowing the robot to autonomously discard noisy data and enhance its positioning based on BIM data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If BIM data and distance measurements are used for localization, then the method is simple to implement, but the localization precision deteriorates due to interfering objects like persons, power tools, and material depositions
Solution Approach 1:
The patent applies segmentation by dividing the environment data into semantic classes (foreground and background) to separate interfering objects from relevant localization features. The semantic classifier segments point cloud data into classes such as persons, furniture, walls, and floors, allowing the system to selectively use only background elements for localization, thereby maintaining precision in cluttered environments while keeping the overall approach manageable
Solution Approach 2:
The patent extracts only the necessary subset of environment data for localization by filtering out foreground objects that interfere with positioning. The semantic classifier identifies and extracts background elements (walls, floors, ceilings) from the complete environment scan, discarding interfering objects like persons and equipment, thus achieving precise localization using only relevant data
2Reliability
If a semantic classifier with updatable model is used to filter environment data, then the localization robustness improves in cluttered environments, but the device complexity increases
Solution Approach 1:
The semantic classifier implements self-service through automatic model updating using the environment data itself. The system learns from the captured environment data without requiring manual training or external intervention, automatically adapting to different construction sites and configurations. This self-updating capability maintains high robustness while reducing the operational complexity of deploying the system in varied environments
Solution Approach 2:
The system performs preliminary semantic classification and model updating before localization occurs. By pre-processing the environment data to identify and classify objects, and updating the semantic model in advance based on captured data, the system prepares the filtered environment data for more accurate and robust localization execution
3Quantity of substance
If all environment data is used for localization, then the data availability is maximized, but the localization precision deteriorates due to noisy and interfering data from construction site clutter
Solution Approach 1:
The patent extracts only the necessary subset of environment data for localization by filtering out foreground objects that interfere with positioning. The semantic classifier identifies and extracts background elements (walls, floors, ceilings) from the complete environment scan, discarding interfering objects like persons and equipment, thus achieving precise localization using only relevant data
Solution Approach 2:
The patent applies different quality treatments to different parts of the environment data. Background elements receive high-quality processing for localization, while foreground objects are filtered out or down-weighted. This local differentiation in data quality handling ensures that precise localization is achieved using only the high-quality background data while maintaining comprehensive environmental awareness
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 enables robust and precise localization of mobile construction robots, even in cluttered environments, by selectively using environment data and continuously updating the semantic model, thereby improving the robot's autonomy and accuracy.
Implementation Method 1
The environment data may be obtained by distance measurement sensors, e. g. by a LIDAR scanner or a time-of-flight camera.
Data Source
Figure 1
Figure 2A~2D
Figure 3
AI summary
The invention concerns a method (10) of localizing a mobile construction robot (102) on a construction site (101), wherein environment data (12) of the construction site is captured, wherein a pose of the mobile robot (102) is inferred using the environment data (12), wherein the method (10) comprises a step of semantic segmentation, wherein a semantic classifier (18) having an updatable semantic model classifies the environment data (12) into at least two semantic classes, wherein the semantic model is updated at least once. Furthermore, the invention concerns a mobile construction robot system (100) and a computer program product (108). The invention provides solutions for a robust and precise localization of the mobile construction robot (102).