Robot Sensor Pose Calibration Using Scan Matching Feedback
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Solution Overview
Problem
Robots face challenges in maintaining accurate sensor calibration over time due to wear and tear, collisions, and other perturbations, which affects their safe and precise navigation and operation.
Innovation Solution
A method is disclosed for determining the pose of sensors on a robot using a controller to calculate a sensor transformation matrix through scan matching between measurements from multiple sensors, optimizing a pose graph to correct sensor data and apply digital transformations, ensuring sensors remain calibrated.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If sensors are used for robot navigation and operation, then the robot can perform tasks autonomously, but the sensor calibration drifts over time due to wear and tear and collisions
Solution Approach 1:
The system continuously monitors sensor data from multiple sensors and uses scan matching algorithms to detect calibration drift. The pose graph optimization process provides feedback to identify and correct sensor pose deviations, maintaining calibration accuracy over time through iterative refinement of sensor transformation matrices.
Solution Approach 2:
The robot performs self-calibration by autonomously collecting sensor data, computing scan matches between sensors, optimizing pose graphs, and applying digital transformations to correct calibration drift without requiring external intervention or manual recalibration.
2Measurement precision
If multiple sensors are used to improve measurement accuracy, then navigation precision increases, but the complexity of sensor calibration and data alignment increases
Solution Approach 1:
The system merges data from multiple sensors by computing scan matches between them and integrating measurements through pose graph optimization. This combines the strengths of multiple sensors to achieve high measurement precision while managing calibration complexity through unified processing.
Solution Approach 2:
The base link frame serves as an intermediary reference frame that facilitates alignment between multiple sensors. By transforming all sensor measurements to the base link frame and using it as a common reference, the system simplifies the calibration process while maintaining high measurement accuracy across all sensors.
3Measurement precision
If real-time sensor calibration is performed to maintain precision, then navigation accuracy is preserved, but computational resources and processing time are consumed
Solution Approach 1:
The system performs partial calibration by focusing computational resources on optimizing the pose graph and correcting sensor poses that exhibit drift, rather than recalibrating all sensors continuously. This selective approach maintains precision while reducing overall computational energy consumption.
Solution Approach 2:
The system performs preliminary scan matching between sensors to identify calibration drift before it significantly impacts navigation accuracy. By detecting and correcting pose deviations early through continuous monitoring, the system maintains precision while avoiding the need for more intensive computational correction later.
Data Source
AI summary
Systems and methods for determining a pose of a sensor on a robot are disclosed herein. According to at least one non-limiting exemplary embodiment a pose of a sensor may be determined with respect to a base link frame origin based on a measured discrepancy between localization data of an object by the sensor and another sensor, the discrepancy corresponding to an error in a pose of the sensor. Pose graph optimization may further be utilized to calibrate all sensors of a robot using digital transformations or may be utilized to diagnose errors in poses of one or more sensors.