Robot Localization Maps Using BIM Semantic Permanence
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Robots deployed in dynamically changing environments face challenges with localization and navigation due to reliance on non-permanent objects, leading to inaccurate maps and compromised operations, requiring manual and resource-intensive updates from operators.
Innovation Solution
Utilizing a semantic model, such as a Building Information Model (BIM), to provide precise information on permanent objects and their schedules, enabling robots to autonomously update localization maps and avoid non-permanent objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If robots rely on non-permanent objects for localization and navigation, then they can operate in dynamic environments, but the localization accuracy deteriorates and manual updates are required
Solution Approach 1:
The system performs preliminary classification of objects as permanent or non-permanent before using them for localization. By pre-processing sensor data to identify permanent objects (such as structural elements) and excluding non-permanent objects (such as temporary equipment), the system establishes reliable localization references in advance, avoiding the need for manual updates when the environment changes.
2Reliability
If robots use manual updates from operators to maintain localization maps, then localization accuracy can be maintained, but the operational efficiency decreases and resource consumption increases
Solution Approach 1:
The robot autonomously maintains its localization map by continuously classifying objects as permanent or non-permanent using sensor data and machine learning algorithms. The system self-corrects its localization references without human intervention, automatically adapting to environmental changes while maintaining accuracy, thereby eliminating the need for operator updates and preserving operational efficiency.
3Reliability
If robots continuously update localization maps with new permanent objects, then navigation reliability improves, but the computational resources and time consumption increase
Solution Approach 1:
The system applies different processing qualities to different objects based on their permanence classification. Permanent objects undergo rigorous verification and integration into the localization map, while non-permanent objects are quickly identified and excluded. This differentiated approach ensures navigation reliability through proper permanent object integration while minimizing time consumption by rapidly filtering out transient objects.
Data Source
AI summary
A method includes receiving, while a robot traverses a building environment, sensor data captured by one or more sensors of the robot. The method includes receiving a building information model (BIM) for the environment that includes semantic information identifying one or more permanent objects within the environment. The method includes generating a plurality of localization candidates for a localization map of the environment. Each localization candidate corresponds to a feature of the environment identified by the sensor data and represents a potential localization reference point. The localization map is configured to localize the robot within the environment when the robot moves throughout the environment. For each localization candidate, the method includes determining whether the respective feature corresponding to the respective localization candidate is a permanent object in the environment and generating the respective localization candidate as a localization reference point in the localization map for the robot.