AR Tag Localization for Mobile Robots in Feature-Poor Indoor Navigation
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Solution Overview
Problem
Mobile robots experience mislocalization issues due to sensor noise, failure, or challenging environments with no features or high symmetry, leading to mission failure and the need for human intervention.
Innovation Solution
A mobile robot system that utilizes an imaging sensor, motors, and a controller to detect single or multiple AR tags, applying specific algorithms for pose data calculation and navigation, enabling autonomous correction of its pose in real time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a mobile robot uses sensor systems for navigation in indoor environments, then the robot can autonomously move and perform missions, but the robot experiences mislocalization issues due to sensor noise, failure, or challenging environments
Solution Approach 1:
The patent introduces AR tags as intermediary objects in the environment that serve as reliable reference points for robot localization. These tags are detected by the imaging sensor and provide stable positional information that mediates between the robot's sensor system and the environment, correcting mislocalization without requiring direct environmental feature interpretation
Solution Approach 2:
The patent replaces reliance on traditional sensor-based localization (which fails in challenging environments) with a vision-based AR tag detection system. This substitution uses image processing and computer vision algorithms to determine robot pose from detected tag positions, providing more reliable localization in indoor environments with sensor noise or failure
2Measurement precision
If the robot applies different tag detection algorithms for single tag vs multiple tags, then the robot improves positioning precision, but the device complexity increases
Solution Approach 1:
The patent applies different tag detection algorithms based on the local condition of tag quantity in the image. When a single tag is detected, one algorithm is used; when multiple tags are detected, a different algorithm is applied. This local adaptation optimizes positioning precision for each specific scenario without requiring a single complex solution for all cases
3Reliability
If the robot corrects its pose in real time using AR tag detection, then the robot prevents mislocalization and ensures mission completion, but the processing time and computational load increase
Solution Approach 1:
The patent implements periodic AR tag detection and pose correction during robot navigation. The imaging sensor continuously captures images and the controller periodically detects tags and updates robot pose estimates. This periodic action ensures real-time correction of mislocalization while maintaining manageable processing loads by not requiring continuous computation at maximum intensity
Data Source
AI summary
A robot includes a controller programmed to: when a single tag is detected in an image captured by an imaging sensor, apply a first tag detection algorithm to the image to obtain pose data of the single tag; when two or more tags are detected in the image, apply a second tag detection algorithm to the image to obtain pose data of the two or more tags; obtain pose data of the single tag in a map frame or pose data of the two or more tags in the map frame; determine pose data of the robot in the map frame based on a comparison of the pose data of the tag in the image and the pose data of the tag in the map frame; and operate one or more motors to autonomously navigate the robot based on the pose data of the robot in the map frame.