Kalman Filter - Part 2
Course Link: https://www.coursera.org/learn/state-estimation-localization-self-driving-cars
Let's consider our Kalman Filter from the previous lesson and use it to estimate the position of our autonomous car. If we have some way of knowing the true position of the vehicle, for example, an oracle tells us, we can then use this to record a position error of our filter at each time step k. Since we're dealing with random noise, doing this once is not enough. We'll need to repeat this same process over and over and record our position error at each time step. Once we've collected these errors, if they average to zero at a particular time step k, then we say the Kalman Filter estimate is unbiased at this time step. Graphically, this is what the situation may look like. Say the particular time step, we know that the true position is the following...
Видео Kalman Filter - Part 2 канала Machine Learning TV
Let's consider our Kalman Filter from the previous lesson and use it to estimate the position of our autonomous car. If we have some way of knowing the true position of the vehicle, for example, an oracle tells us, we can then use this to record a position error of our filter at each time step k. Since we're dealing with random noise, doing this once is not enough. We'll need to repeat this same process over and over and record our position error at each time step. Once we've collected these errors, if they average to zero at a particular time step k, then we say the Kalman Filter estimate is unbiased at this time step. Graphically, this is what the situation may look like. Say the particular time step, we know that the true position is the following...
Видео Kalman Filter - Part 2 канала Machine Learning TV
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