Range sensors (for example sonar, infrared, and lidar) are notoriously noisy. How can I characterize the noise characteristics to include these in a probabilistic localization sensor model?
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2 Answers
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This subject is covered quite nicely in the Probabilistic Robotics book by Thrun et. al. I don't have a direct reference, but there are some of his papers (such as Robust Monte Carlo Localization for Mobile Robots, pdf) essentially include the same information. Usually what is used is a mixed error model, where the probability density function consists of different parts
- A Gaussian error around the true distance reading
- A part which accounts for false positives like dynamic obstacles and so on. This is larger with smaller distances.
- A constant part which accounts for false negative readings, where the sensor gives an out of range reading.
The model needs to be fitted to your sensor and application.
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Almost everybody just assumes the noise is gaussian because that way the math is relatively easy.
If you really wanted, you could experimentally determine the distribution of sensor noise, fit a model to it, and use that but it would be a lot of work for potentially no gain.
