Automatic Map Annotation for Robot Navigation
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Solution Overview
Problem
Current systems for annotating maps used by robots and autonomous agents lack efficiency in automatically identifying and marking keepout zones and obstacles, leading to potential safety hazards and inaccuracies in navigation.
Innovation Solution
A system and method that utilize a server connected to a robot, with a graphic user interface, to perform automatic map annotation through flood fills, dilation, erosion, and convex hull creation, allowing for the identification and marking of obstacles and keepout zones based on sensor data, incorporating both robotic and human input for accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual annotation methods are used for map annotation, then accuracy can be maintained through human judgment, but productivity is reduced due to time-consuming manual processes
Solution Approach 1:
The system enables automatic self-annotation of maps by robots using computer vision and machine learning algorithms. The robot independently identifies obstacles, determines keepout zones, and annotates the map without human intervention, achieving both high productivity and maintained accuracy through automated decision-making algorithms
Solution Approach 2:
Manual mechanical annotation processes are replaced with automated computer vision systems and machine learning models. The system uses sensors, image processing, and algorithms to automatically detect obstacles and annotate maps, substituting human manual work with automated computational processes that maintain accuracy while dramatically improving efficiency
2Productivity
If automatic annotation algorithms are implemented, then productivity increases through automated processing, but measurement precision may deteriorate due to algorithmic limitations
Solution Approach 1:
The system incorporates feedback mechanisms where annotation results are continuously evaluated and refined. Machine learning models learn from annotated data, improving their accuracy over time. The system uses sensor feedback to verify obstacle detections and adjusts algorithms based on performance metrics, ensuring precision improves with automated processing
Solution Approach 2:
The system performs preliminary processing of sensor data before final annotation, using multiple algorithms in sequence. Pre-processing steps include noise filtering, feature extraction, and preliminary obstacle detection that prepare data for more accurate final annotation, maintaining precision while enabling automated high-speed processing
3Measurement precision
If complex annotation algorithms are used to improve accuracy, then measurement precision increases, but device complexity increases requiring more computational resources
Solution Approach 1:
The annotation system is divided into separate modular components: obstacle detection module, keepout zone determination module, map annotation module, and validation module. Each component performs a specific function with optimized computational requirements, allowing complex accurate annotation while managing system complexity through modular architecture
Solution Approach 2:
The system applies different levels of processing complexity to different regions of the map based on local requirements. High-precision algorithms are applied only where needed (e.g., near robot position, in areas with detected obstacles), while other regions use simpler processing, maintaining accuracy where required while reducing overall computational complexity
4Measurement precision
If more sensor data is collected to improve annotation accuracy, then measurement precision increases, but loss of time increases due to data processing requirements
Solution Approach 1:
The system collects and processes only the necessary amount of sensor data required for accurate annotation, avoiding excessive data collection. It uses selective sensing that focuses on relevant areas (e.g., forward view, areas with detected obstacles) rather than processing all possible sensor data, maintaining accuracy while reducing processing time through targeted data acquisition
Data Source
AI summary
A system for automatically annotating a map includes: a robot; a server operably connected to the robot; file storage configured to store files, the file storage operably connected to the server; an annotations database operably connected to the server, the annotations database comprising map annotations; an automatic map annotation service operably connected to the server, the automatic map annotation service configured to automatically do one or more of create a map of an item of interest and annotate a map of an item of interest; a queue of annotation requests operably connected to the automatic annotation service; and a computer operably connected to the server, the computer comprising a graphic user interface (GUI) usable by a human user.