Add docs from pypose/pypose

Author: pyposebot

docs/preview/pr/363/_modules/pypose/module/ekf/index.html

@@ -401,7 +401,7 @@ <h1>Source code for pypose.module.ekf</h1><div class="highlight"><pre>
 
         <span class="n">P</span> <span class="o">=</span> <span class="n">A</span> <span class="o">@</span> <span class="n">P</span> <span class="o">@</span> <span class="n">A</span><span class="o">.</span><span class="n">mT</span> <span class="o">+</span> <span class="n">Q</span>                 <span class="c1"># 2. Covariance predict</span>
         <span class="n">K</span> <span class="o">=</span> <span class="n">P</span> <span class="o">@</span> <span class="n">C</span><span class="o">.</span><span class="n">mT</span> <span class="o">@</span> <span class="n">pinv</span><span class="p">(</span><span class="n">C</span> <span class="o">@</span> <span class="n">P</span> <span class="o">@</span> <span class="n">C</span><span class="o">.</span><span class="n">mT</span> <span class="o">+</span> <span class="n">R</span><span class="p">)</span> <span class="c1"># 3. Kalman gain</span>
-        <span class="n">e</span> <span class="o">=</span> <span class="n">y</span> <span class="o">-</span> <span class="n">bmv</span><span class="p">(</span><span class="n">C</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span> <span class="o">-</span> <span class="n">bmv</span><span class="p">(</span><span class="n">D</span><span class="p">,</span> <span class="n">u</span><span class="p">)</span>    <span class="c1">#    predicted observation error</span>
+        <span class="n">e</span> <span class="o">=</span> <span class="n">y</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">observation</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">t</span><span class="o">=</span><span class="n">t</span><span class="p">)</span>    <span class="c1">#    predicted observation error</span>
         <span class="n">xp</span> <span class="o">=</span> <span class="n">xm</span> <span class="o">+</span> <span class="n">bmv</span><span class="p">(</span><span class="n">K</span><span class="p">,</span> <span class="n">e</span><span class="p">)</span>                     <span class="c1"># 4. Posteriori state</span>
         <span class="n">P</span> <span class="o">=</span> <span class="p">(</span><span class="n">I</span> <span class="o">-</span> <span class="n">K</span> <span class="o">@</span> <span class="n">C</span><span class="p">)</span> <span class="o">@</span> <span class="n">P</span>                   <span class="c1"># 5. Posteriori covariance</span>
         <span class="k">return</span> <span class="n">xp</span><span class="p">,</span> <span class="n">P</span></div>