Vision Sensor Orientation Reconciliation for Mobile Robots
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Solution Overview
Problem
Existing methods for reconciling disparate orientations of multiple vision sensors on mobile robots are resource-intensive, unreliable, and not scalable, often requiring ground truth positional data and visual indicia with known dimensions, which limits their applicability in dynamic environments and makes them cumbersome for resource-constrained robots.
Innovation Solution
The techniques involve analyzing digital images captured by multiple vision sensors to identify common features and calculate the major direction of feature movement, allowing for the reconciliation of sensor orientations without ground truth knowledge, using feature detection and matching methods to adjust the orientations of the sensors or their images, enabling alignment of major directions of feature movement across multiple image streams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional calibration methods using visual indicia and ground truth data are employed, then measurement precision of sensor orientations is improved, but device complexity and resource consumption increase
Solution Approach 1:
The system performs self-calibration by automatically detecting features in the environment and computing sensor orientations without requiring external calibration tools, ground truth data, or manual intervention. The robot uses its own sensor data to reconcile orientation discrepancies between multiple vision sensors through iterative optimization algorithms.
Solution Approach 2:
The patent extracts and removes the dependency on external calibration artifacts (checkerboards, visual indicia) and ground truth positional data (odometry, GPS) from the calibration process. By eliminating these external requirements, the system achieves calibration using only the robot's own sensor measurements and environmental features.
2Manufacturing precision
If multiple vision sensors are mounted with precise hardware constraints, then manufacturing precision is improved, but ease of manufacture deteriorates
Solution Approach 1:
The system changes the approach from controlling physical mounting parameters (hardware constraints, dimensional tolerances) to controlling computational parameters (orientation reconciliation algorithms). By allowing flexible hardware mounting and compensating through software-based orientation correction, the system eliminates the need for precision manufacturing and assembly.
3Reliability
If resource-intensive calibration methods are used, then reliability of orientation data is improved, but productivity deteriorates
Solution Approach 1:
The system achieves reliable orientation data through automated self-calibration that operates in real-time during normal robot operation. The continuous feature-based calibration eliminates the need for separate calibration procedures, trained personnel, or ground truth data, enabling immediate deployment while maintaining high reliability through ongoing automatic optimization.
Data Source
AI summary
Implementations are described herein are directed to reconciling disparate orientations of multiple vision sensors deployed on a mobile robot (or other mobile vehicle) by altering orientations of the vision sensors or digital images they generate. In various implementations, this reconciliation may be performed with little or no ground truth knowledge of movement of the robot. Techniques described herein also avoid the use of visual indicia of known dimensions and/or other conventional tools for determining vision sensor orientations. Instead, techniques described herein allow vision sensor orientations to be determined and/or reconciled using less resources, and are more scalable than conventional techniques.