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Statistical confidence is most often defined in terms of a score that is the number of standard deviations away from the mean. Now let's say you have a set of states (a state vector) used to define an object recognized using image processing in OpenCV (for example, RGB data, HSV data, filter responses, etc.). Given a series of training images for vehicles, ...


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Short answer: You need at least two measurements and you should use many more. Definitions The standard deviation is defined as $$\sigma = \mathbb{E}[x^2]-\mathbb{E}[x]^2,$$ which is the difference between the first and second moments of $x$. The sample standard deviation estimates this using $N+1$ samples, $$\hat{\sigma} = \sqrt{\frac{1}{N}\sum_{i=0}^...


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You can start by using split-and-merge segmentation algorithm. There are many algorithms available A Comparison of Line Extraction Algorithms using 2D Laser Rangefinder for Indoor Mobile Robotics. If you want to refer the code, A ROS package is available here and below picture is the result from it.


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Our goal is to find a recursive expression for $ P\left(\mathrm{x}_{k},\mathrm{m} | \mathrm{Z}_{0:k}, \mathrm{U}_{0:k},\mathrm{x}_{0}\right)$. This expression is called the belief for the robot's state $\mathrm{x}_{k}$ and the map $\mathrm{m}$ given all the measurements $\mathrm{Z}_{0:k} = (\mathrm{z}_{0},..., \mathrm{z}_{k})$, the control actions $\mathrm{U}...


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From technical point of view I would refer you to this pcl tutorial From you description I guess that you want to navigate the car between lines. Looking a one scan and detecting the line is quite crude, a more complex approach would be to merge the LIDAR info with odometry info and use a SLAM algorithm to at the same time build a map of the environment and ...


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In short; no, you can't estimate standard deviation from a single measurement. Your last equation, assuming $\mu = x_{gt}$ is invalid because you have no guarantee that the mean measurement will match the ground truth (the sensor could have bias error). The first formula you give is used only if you sample the entire population of possible values, which ...


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Standard deviation is used to represent probability distribution - in your case if you have $\sigma = 1m$ for forward distance it means that there is ~68% chance of true forward distance to the obstacle be less than 1m away from your measurement. You have two variables, so you should project your probability distribution on a 2D surface - you will get ...


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