Fixed bug in EKF residual function.

Comment was wrong, it normalizes to [-pi, pi]. Also, code didn't handle cases where a-b > 2Pi
2015-07-15 19:03:59 -07:00
1 parent 7827a4df2d
commit 335df7f42d
@@ -3433,7 +3433,7 @@
   "source": [
    "def normalize_angle(x):\n",
    "    x = x % (2 * np.pi)    # force in range [0, 2 pi)\n",
-    "    if x > np.pi:          # move to [-pi, pi]\n",
+    "    if x > np.pi:          # move to [-pi, pi)\n",
    "        x -= 2 * np.pi\n",
    "    return x"
   ]
@@ -16,11 +16,19 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    },
    {
     "data": {
      "text/html": [
@@ -254,7 +262,7 @@
       "<IPython.core.display.HTML object>"
      ]
     },
-     "execution_count": 1,
+     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -309,7 +317,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
@@ -318,7 +326,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x78bd8d0>"
+       "<matplotlib.figure.Figure at 0x9fee5f8>"
      ]
     },
     "metadata": {},
@@ -357,7 +365,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
@@ -366,7 +374,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x794d2e8>"
+       "<matplotlib.figure.Figure at 0x9fcc630>"
      ]
     },
     "metadata": {},
@@ -431,7 +439,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
@@ -440,7 +448,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x59d8908>"
+       "<matplotlib.figure.Figure at 0xaf4b898>"
      ]
     },
     "metadata": {},
@@ -646,7 +654,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
@@ -671,7 +679,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
@@ -694,7 +702,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
@@ -769,16 +777,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x81addd8>"
+       "<matplotlib.figure.Figure at 0x7896320>"
      ]
     },
     "metadata": {},
@@ -786,9 +794,9 @@
    },
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8245240>"
+       "<matplotlib.figure.Figure at 0xad3fb00>"
      ]
     },
     "metadata": {},
@@ -796,9 +804,9 @@
    },
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x82930f0>"
+       "<matplotlib.figure.Figure at 0xafdf6a0>"
      ]
     },
     "metadata": {},
@@ -861,7 +869,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "scrolled": true
@@ -881,7 +889,7 @@
       "⎣╲╱  x_alt  + x_pos       ╲╱  x_alt  + x_pos  ⎦"
      ]
     },
-     "execution_count": 9,
+     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -946,7 +954,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
@@ -973,7 +981,7 @@
       "⎣          2                                                          ⎦"
      ]
     },
-     "execution_count": 10,
+     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -1008,7 +1016,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "scrolled": true
@@ -1018,7 +1026,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x81aec18>"
+       "<matplotlib.figure.Figure at 0xacfc4a8>"
      ]
     },
     "metadata": {},
@@ -1143,7 +1151,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "scrolled": false
@@ -1170,7 +1178,7 @@
       "⎣0  0                  1                 ⎦"
      ]
     },
-     "execution_count": 12,
+     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -1210,7 +1218,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
@@ -1260,7 +1268,7 @@
       "                                                  ⎦"
      ]
     },
-     "execution_count": 13,
+     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -1320,7 +1328,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
@@ -1343,7 +1351,7 @@
       "⎣  (px - x)  + (py - y)        (px - x)  + (py - y)        ⎦"
      ]
     },
-     "execution_count": 14,
+     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -1366,7 +1374,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
@@ -1400,7 +1408,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
@@ -1451,7 +1459,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
@@ -1528,20 +1536,19 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def residual(a,b):\n",
-    "    \"\"\" compute residual between two measurement containing [range, bearing]. Bearing\n",
-    "    is normalized to [0, 360)\"\"\"\n",
+    "    \"\"\" compute residual (a-b) between two measurement containing \n",
+    "    [range, bearing]. Bearing is normalized to [-pi, pi)\"\"\"\n",
    "    y = a - b\n",
-    "    if y[1] > np.pi:\n",
-    "        y[1] -= 2*np.pi\n",
-    "    if y[1] < -np.pi:\n",
-    "        y[1] += 2*np.pi\n",
+    "    y[1] = y[1] % (2 * np.pi)    # force in range [0, 2 pi)\n",
+    "    if y[1] > np.pi:          # move to [-pi, pi)\n",
+    "        y[1] -= 2 * np.pi\n",
    "    return y"
   ]
  },
@@ -1554,7 +1561,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "scrolled": true
@@ -1603,7 +1610,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "scrolled": true
@@ -1611,9 +1618,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8289710>"
+       "<matplotlib.figure.Figure at 0xaf7d5c0>"
      ]
     },
     "metadata": {},
@@ -1623,7 +1630,7 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "[ 0.02411999  0.04207742  0.00218955]\n"
+      "[ 0.02421568  0.04097243  0.00219456]\n"
     ]
    }
   ],
@@ -1638,16 +1645,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8289320>"
+       "<matplotlib.figure.Figure at 0xafa59b0>"
      ]
     },
     "metadata": {},
@@ -1657,7 +1664,7 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "[ 0.02018057  0.02053449  0.00154182]\n"
+      "[ 0.02009079  0.02039044  0.00153318]\n"
     ]
    }
   ],
@@ -1682,12 +1689,30 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x28a15f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.02037365  0.04595254  0.0002267 ]\n"
+     ]
+    }
+   ],
   "source": [
    "ekf = run_localization(landmarks[0:2], sigma_vel=1.e-10, sigma_steer=1.e-10,\n",
    "                       sigma_range=1.4, sigma_bearing=.05)\n",
@@ -1703,12 +1728,30 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xacd1198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.2535206   0.80589457  0.00354734]\n"
+     ]
+    }
+   ],
   "source": [
    "ekf = run_localization(landmarks[0:1], sigma_vel=1.e-10, sigma_steer=1.e-10,\n",
    "                       sigma_range=1.4, sigma_bearing=.05)\n",
@@ -1724,12 +1767,30 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x9e37390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.00841848  0.00915344  0.00077199]\n"
+     ]
+    }
+   ],
   "source": [
    "landmarks = array([[5, 10], [10,  5], [15, 15], [20,  5], [15, 10], \n",
    "                   [10,14], [23, 14], [25, 25], [10, 20]])\n",
@@ -1769,11 +1830,30 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAAEWCAYAAAC+BfslAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa286208>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "actual mean=1.29, std=1.12\n",
+      "EKF    mean=1.00, std=0.95\n"
+     ]
+    }
+   ],
   "source": [
    "import nonlinear_plots\n",
    "nonlinear_plots.plot_ekf_vs_mc()"
@@ -1790,11 +1870,30 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xaa6d5f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "actual mean=1.30, std=1.13\n",
+      "UKF    mean=1.30, std=1.08\n"
+     ]
+    }
+   ],
   "source": [
    "nonlinear_plots.plot_ukf_vs_mc(alpha=0.001, beta=3., kappa=1.)"
   ]
@@ -12,7 +12,7 @@ sys.path.insert(0,'./code') # allow us to import book_format

 def test_filterpy_version():
    import filterpy
-    min_version = [0,0,22]
+    min_version = [0,0,25]
    v = filterpy.__version__
    tokens = v.split('.')
    for i,v in enumerate(tokens):