Robot Image Stitching With Depth-Based Seam Placement
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Solution Overview
Problem
Robotic devices face challenges in generating accurate and artifact-free image data during navigation due to parallax errors from combining images captured by sensors with different poses, orientations, and translations, which can lead to inefficiencies and inaccurate environmental representations.
Innovation Solution
A method and system for robotic devices to determine distances between the robot and its environment using sensor data, adjust image combinations based on these distances, and reduce parallax by strategically placing seams in images to minimize artifacts, thereby enhancing the accuracy and efficiency of image data generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If images are combined from sensors with different poses and orientations, then the field of view is expanded and environmental coverage is improved, but parallax errors and artifacts are introduced that reduce image accuracy
Solution Approach 1:
The system dynamically adjusts the seam placement parameter based on depth information. By changing the seam location from a fixed position to a depth-dependent position, the system resolves parallax artifacts while maintaining expanded field of view coverage.
Solution Approach 2:
The system performs preliminary depth estimation using sensor data before combining images. This advance calculation of depth information allows the system to pre-determine optimal seam locations that minimize parallax effects before the actual image combination occurs.
2Loss of information
If multiple images are combined to create comprehensive environmental representations, then the completeness of environmental data is improved, but processing time and computational complexity increase
Solution Approach 1:
The system segments the image combination process into distinct stages: depth estimation, seam location calculation, and image blending. This segmentation allows for optimized processing at each stage and enables parallel computation where applicable.
Solution Approach 2:
The system replaces complex manual image alignment procedures with automated depth-based seam placement algorithms. This substitution of mechanical/image processing methods with computational algorithms reduces processing time while maintaining data completeness.
3Device complexity
If images are combined without adjusting for depth differences, then processing simplicity is maintained, but parallax artifacts degrade the quality of environmental representations
Solution Approach 1:
The system introduces depth information as an intermediary element between the multiple images and the final combined output. This intermediary depth map guides the seam placement process, enabling automatic adjustment for parallax without requiring complex manual intervention.
4Manufacturing precision
If seam placement is optimized to reduce parallax artifacts, then image quality is improved, but the complexity of the combination algorithm increases
Solution Approach 1:
The system changes the seam placement parameter from a fixed or heuristic-based value to a depth-dependent variable. This parameter change enables automatic optimization of seam locations based on actual scene geometry, improving image quality through a relatively simple computational approach.
Data Source
AI summary
Systems and methods are described for combining sensor data obtained by a mobile robot. A system can obtain first sensor data from one or more first sensors of a robot. The system can determine a distance between the robot and at least a portion of the environment based on the first sensor data. For example, the distance may be a depth from a depth map. The system can obtain second sensor data from one or more second sensors of the robot. The system can combine a first portion of the second sensor data and a second portion of the second sensor data based on the distance. For example, the system can use the distance to determine a seam for combination of the first image and the second image. The system can instruct output of a user interface based on the combination.