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Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...

Uncertainty in Measurement: Accuracy and Precision

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.

The Uncertainty Principle

The Uncertainty Principle

Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...

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Semantic Point Cloud Mapping of LiDAR Based on Probabilistic Uncertainty Modeling for Autonomous Driving.

Sungjin Cho¹, Chansoo Kim¹, Jaehyun Park¹

¹Department of Automotive Engineering, Hanyang University, Seoul 04763, Korea.

Sensors (Basel, Switzerland)

|October 22, 2020

Summary

This study introduces a novel LiDAR SLAM system that incorporates semantic information to improve autonomous driving. By modeling segmentation uncertainty, it enhances mapping accuracy and resolves semantic ambiguities.

Keywords:

LiDAR autonomous vehicle deep learning-based semantic segmentation graph SLAM semantic point cloud mapping uncertainty probability

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Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

LiDAR-based Simultaneous Localization And Mapping (SLAM) is crucial for autonomous vehicles.
Semantic information enhances SLAM capabilities beyond geometric data.
Uncertainty in semantic segmentation limits direct application in LiDAR SLAM.

Purpose of the Study:

To propose a semantic segmentation-based LiDAR SLAM system that addresses segmentation uncertainty.
To improve the accuracy and reliability of map generation for autonomous driving.

Main Methods:

Developed probability models from data-driven approaches to quantify semantic segmentation uncertainty.
Introduced a semantic registration method using these probability models to calculate point cloud transformations.
Utilized probability models to resolve semantic class ambiguities in multi-scan point clouds.

Main Results:

The proposed framework effectively reduces mapping pose errors.
Ambiguity in semantic information within the generated semantic map is eliminated.
Experimental validation on the KITTI dataset and outdoor environments demonstrated system efficacy.

Conclusions:

The developed system successfully integrates semantic information into LiDAR SLAM by managing segmentation uncertainty.
This approach significantly enhances the performance and reliability of autonomous driving systems.