Robotic Panoramic Imagery Construction via Feature Anchoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for constructing high-resolution panoramic imagery on robotic devices face challenges such as computational inefficiency and inaccuracies in feature identification, particularly with small labels and feature-poor visual scenes, due to reliance on image-element stitching methods that fail in aligning similar images with repeated features and interpolating smaller features.
Innovation Solution
The system employs a robotic system with a processor that receives images via sensors, determines distances and translations, aligns images using a computer-readable map, and adjusts image quality based on contrast and color values, ensuring accurate panoramic image construction by leveraging robotic odometry and image quality matrices to prevent label duplication or skipping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image-element stitching methods are used to construct panoramic imagery, then the panoramic image can be formed from multiple images, but the method fails in aligning similar images with repeated features and interpolating smaller features, leading to inaccuracies in feature identification
Solution Approach 1:
The patent introduces an intermediary alignment process that uses detected features (labels, text, codes) as reference points to mediate the stitching of panoramic images. Instead of directly stitching images based on visual similarity, the system first detects features in each image, uses these features as anchors, and aligns images based on the geometric relationships between corresponding features. This intermediary feature-based alignment step resolves the contradiction by providing reliable correspondence points even in images with repeated features or poor visual characteristics.
Solution Approach 2:
The system implements feedback by detecting features in the captured images, using the detected feature positions and characteristics to guide the alignment process, and then verifying the alignment accuracy by checking the consistency of feature positions in the stitched panoramic image. This feedback loop allows the system to iteratively refine the alignment, improving measurement precision while maintaining reliability in feature identification even for small labels and feature-poor scenes.
2Manufacturing precision
If multiple images are captured and stitched to form high-resolution panoramic imagery, then the resolution and coverage are improved, but the computational load increases due to the complexity of aligning and processing multiple images
Solution Approach 1:
The patent applies preliminary action by detecting and identifying features (labels, text, codes) in each individual image before the stitching process. By pre-processing the images to extract and catalog feature positions, characteristics, and relationships, the system reduces the computational complexity of the subsequent alignment and stitching operations. This preliminary feature extraction creates a simplified representation that guides the panoramic construction, maintaining high resolution while improving computational efficiency.
Solution Approach 2:
The system segments the panoramic image construction process into distinct stages: feature detection in individual images, feature matching across images, alignment calculation based on matched features, and final stitching. This segmentation allows each stage to be optimized independently, reducing overall computational load while maintaining high-resolution output. The feature-based segmentation approach is particularly efficient for handling repeated features and feature-poor scenes.
3Ease of manufacture
If traditional stitching methods are used, then the process is simpler to implement, but small labels and features are lost or duplicated during the stitching process
Solution Approach 1:
The patent uses detected features as an intermediary layer between the raw images and the final stitched panorama. This intermediary feature representation ensures that labels and small features are explicitly identified and tracked through the stitching process, preventing their loss or duplication. The feature-based intermediary approach maintains implementation feasibility while significantly improving feature completeness compared to direct pixel-based stitching methods.
Data Source
AI summary
Systems and methods for constructing high resolution panoramic imagery for feature identification on robotic devices are disclosed herein. According to at least one non-limiting exemplary embodiment, a robot collects a plurality of images of an environment, these images include large overlap in their visual scenes. Using additional image data from the overlapping images, resolution of labels, price tags, and other inventory tags may be enhanced when constructing panoramic imagery to improve feature identification.