daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Finished imcomplete sentence.

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Roger Labbe 2015-08-22 11:34:53 -07:00
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02-Discrete-Bayes.ipynb
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@ -1404,7 +1404,7 @@
"\n",
"But we are not done. A key lesson from the g-h filters chapter is to never throw information away. If you have information you should use it to improve your estimate. What information are we leaving out? Well, we know what our destination is, and we know what control inputs we are feeding to the wheels of the robot at each moment in time. For example, let's say that once a second we send a movement command to the robot - move left 1 unit, move right 1 unit, or stand still. This is obviously a simplification because I am not taking acceleration into account, but I am not trying to teach control theory. If I send the command 'move left 1 unit' I expect that in one second from now the robot will be 1 unit to the left of where it is now. But, wheels and motors are imperfect. We will assume that it never makes a mistake and goes right when told to go left, so the errors should be relatively small. Thus the robot might end up 0.9 units away, or maybe 1.2 units away. \n",
"\n",
"Now the entire solution is clear. For the dog which was moving independently we assumed that he kept moving in whatever direction he was previously moving. That is a dubious assumption for my dog! Robots are far more predictable. Instead of making a dubious prediction based on assumption of behavior we will feed in the command that we sent to the robot! In other words, when we call `predict()` we will pass in the commanded movement that we gave the robot along with a kernel that describes the likelihood of"
"Now the entire solution is clear. For the dog which was moving independently we assumed that he kept moving in whatever direction he was previously moving. That is a dubious assumption for my dog! Robots are far more predictable. Instead of making a dubious prediction based on assumption of behavior we will feed in the command that we sent to the robot! In other words, when we call `predict()` we will pass in the commanded movement that we gave the robot along with a kernel that describes the likelihood of that movement."
]
},
{