Sample-Based State Estimation With Anti-Aliasing Uncertainty Dilation
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Solution Overview
Problem
State estimation modules in robotics and autonomous vehicles often suffer from aliasing due to large sampling intervals, leading to inaccurate localization and pose estimations.
Innovation Solution
A sample-based estimator is executed on sensor data to optimize probability distribution dilation using a minimum measurement uncertainty value calculated based on the Nyquist-Shannon sampling theorem, thereby minimizing aliasing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the sampling interval of the state estimation module is increased, then the computational complexity is reduced, but aliasing occurs leading to inaccurate localization and pose estimations
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the measurement uncertainty parameter based on the sampling interval. By calculating a minimum measurement uncertainty value using the Nyquist-Shannon sampling theorem and applying it to dilate the probability distribution, the system maintains accurate localization estimates even with larger sampling intervals, thus reducing computational complexity without sacrificing measurement precision
Solution Approach 2:
The patent implements preliminary action by pre-calculating the minimum measurement uncertainty value before executing the sample-based estimator. This pre-computed uncertainty value is then used to optimize the probability distribution dilation, allowing the system to avoid aliasing artifacts before they occur during the main estimation process, thereby maintaining accuracy with reduced computational load
2Productivity
If the sampling interval is increased to reduce computational load, then processing speed improves, but aliasing artifacts corrupt the state estimates
Solution Approach 1:
The system changes the measurement uncertainty parameter dynamically based on the sampling interval configuration. By using the Nyquist-Shannon sampling theorem to calculate a minimum uncertainty value and applying it to dilate probability distributions, the system maintains reliable state estimates even when processing speed is increased through larger sampling intervals
Solution Approach 2:
The patent implements feedback by using the calculated minimum measurement uncertainty value to adjust the probability distribution dilation in the sample-based estimator. This feedback mechanism ensures that the estimation process compensates for the larger sampling interval, maintaining reliability while allowing faster processing
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively minimizes aliasing in sample-based estimators, enhancing the accuracy of localization and pose estimations in robotics and autonomous vehicles without increasing computational resources.
Implementation Method 1
the minimum measurement uncertainty value is determined based on an application of the Nyquist-Shannon sampling theorem to the sample-based estimator
Data Source
AI summary
A system can perform a method that includes receiving sensor data from one or more sensors. The system can execute a sample-based estimator on the sensor data. The sample-based estimator optimizes a probability distribution dilation of the sensor data based on a minimum measurement uncertainty value to minimize aliasing in the sample-based estimator.