Removed erroneous chart from PF chapter.

There was a really misleading chart in the old pf material.
It was destined to be deleted, but I didn't want peoople to
be mislead by it if browsing the chapter on github.

11_Particle_Filters.ipynb

@@ -755,6 +755,9 @@
    "source": [
     "## Monte Carlo Method\n",
     "\n",
+    "\n",
+    "** old stuff - to be deleted/incorporated **\n",
+    "\n",
     "All of this brings us to the particle filter. Consider tracking a robot or a car in an urban environment. For consistency I will use the robot localization problem from the ends of the EKF and UKF chapters. In this problem we tracked a robot that had a sensor that could detect the range and bearing to landmarks. \n",
     "\n",
     "Taking insight from the discussion above we start by creating a several thousand **particles**. Each particle has a position that represents a possible belief of where the robot is in the scene. Suppose that when we initialize the filter we have no knowledge of the location of the robot. We would want to scatter the particles uniformly over the entire scene. If there was a large clump of particles near a specific location that would imply that we were more certain that the robot is there. If you think of all of the particles representing a probability distribution, locations where there are more particles represent a higher belief, and locations with fewer particles represents a lower belief.\n",
@@ -875,10 +878,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "It is difficult to visualize the movement from two static images, so here is an animation.\n",
-    "<img src='animations/13_particle_move.gif'>"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",