Sensor Pose Calibration in Pose Graph Localization
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Solution Overview
Problem
Current methods for mapping and localization in warehouse environments using autonomous robots rely on manual surveys and fixed sensor-to-robot transforms, which are time-consuming and prone to errors, delaying robot deployment and affecting navigation accuracy.
Innovation Solution
Implementing a system that uses simultaneous localization and mapping (SLAM) with a graph-based approach to determine a sensor pose transform that optimizes the cost function associated with marker detections, allowing for real-time calibration and minimization of errors by continuously updating the sensor pose based on new measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual surveys are used for mapping and localization, then navigation accuracy can be improved, but deployment time increases and errors are more frequent
Solution Approach 1:
The patent replaces manual surveying methods with an automated SLAM system that uses sensor data processing and graph optimization algorithms. The computing system automatically performs mapping and localization by processing sensor measurements and optimizing pose graphs, eliminating the need for manual mechanical surveying while maintaining or improving accuracy.
Solution Approach 2:
The system enables the robot to perform its own calibration and localization without external intervention. The autonomous vehicle uses its onboard sensors to automatically determine sensor poses and calibrate its own coordinate transforms through graph optimization, making the system self-sufficient and eliminating deployment time delays.
2Device complexity
If fixed sensor-to-robot transforms are used, then device complexity is reduced, but navigation accuracy deteriorates
Solution Approach 1:
The patent transitions from fixed, static sensor transforms to dynamic, optimized transforms. The system continuously determines and updates sensor pose transforms through graph optimization based on actual sensor measurements and robot poses, allowing the transforms to adapt and optimize themselves rather than remaining fixed, thereby improving accuracy without significantly increasing complexity.
3Reliability
If manual calibration methods are used, then reliability is improved, but productivity decreases
Solution Approach 1:
The patent replaces manual calibration procedures with automated computational methods. The computing system performs calibration by processing sensor data, constructing pose graphs, and optimizing transforms through algorithmic methods, eliminating manual intervention while maintaining calibration reliability through mathematical optimization.
Solution Approach 2:
The system performs calibration continuously and iteratively through graph optimization rather than as a discrete manual step. The pose graph optimization continuously refines sensor poses and transforms as new sensor data becomes available, ensuring calibration is maintained throughout operation rather than being a one-time manual process.
Data Source
AI summary
Examples relate to simultaneous localization and calibration. An example implementation may involve receiving sensor data indicative of markers detected by a sensor on a vehicle located at vehicle poses within an environment, and determining a pose graph representing the vehicle poses and the markers. For instance, the pose graph may include edges associated with a cost function representing a distance measurement between matching marker detections at different vehicle poses. The distance measurement may incorporate the different vehicle poses and a sensor pose on the vehicle. The implementation may further involve determining a sensor pose transform representing the sensor pose on the vehicle that optimizes the cost function associated with the edges in the pose graph, and providing the sensor pose transform. In further examples, motion model parameters of the vehicle may be optimized as part of a graph-based system as well or instead of sensor calibration.