diff --git a/Examples/Stereo/stereo_kitti.cc b/Examples/Stereo/stereo_kitti.cc
index cb8bc40..30cd3b2 100644
--- a/Examples/Stereo/stereo_kitti.cc
+++ b/Examples/Stereo/stereo_kitti.cc
@@ -36,9 +36,9 @@ void LoadImages(const string &strPathToSequence, vector<string> &vstrImageLeft,
 
 int main(int argc, char **argv)
 {
-    if(argc != 4)
+    if(argc != 5 && argc != 6)
     {
-        cerr << endl << "Usage: ./stereo_kitti path_to_vocabulary path_to_settings path_to_sequence" << endl;
+        cerr << endl << "Usage: ./stereo_kitti path_to_vocabulary path_to_settings path_to_sequence target_name path_to_cnn_model" << endl;
         return 1;
     }
 
@@ -50,13 +50,21 @@ int main(int argc, char **argv)
 
     const int nImages = vstrImageLeft.size();
 
+    // path to load model
+    string model_path;
+    if (argc > 5) {
+        model_path = string(argv[5]);
+    }
+
     // Create SLAM system. It initializes all system threads and gets ready to process frames.
-    ORB_SLAM2::System SLAM(argv[1],argv[2],ORB_SLAM2::System::STEREO,true);
+    ORB_SLAM2::System SLAM(argv[1],argv[2],ORB_SLAM2::System::STEREO,true, model_path);
 
     // Vector for tracking time statistics
     vector<float> vTimesTrack;
     vTimesTrack.resize(nImages);
 
+
+
     cout << endl << "-------" << endl;
     cout << "Start processing sequence ..." << endl;
     cout << "Images in the sequence: " << nImages << endl << endl;   
@@ -122,7 +130,7 @@ int main(int argc, char **argv)
     cout << "mean tracking time: " << totaltime/nImages << endl;
 
     // Save camera trajectory
-    SLAM.SaveTrajectoryKITTI("CameraTrajectory.txt");
+    SLAM.SaveTrajectoryKITTI(string(argv[4]));
 
     return 0;
 }
diff --git a/python/rgbd_benchmark_tools/.ipynb_checkpoints/TUM_trajectory_evaluation-checkpoint.ipynb b/python/rgbd_benchmark_tools/.ipynb_checkpoints/TUM_trajectory_evaluation-checkpoint.ipynb
index a8008d2..e18edf9 100644
--- a/python/rgbd_benchmark_tools/.ipynb_checkpoints/TUM_trajectory_evaluation-checkpoint.ipynb
+++ b/python/rgbd_benchmark_tools/.ipynb_checkpoints/TUM_trajectory_evaluation-checkpoint.ipynb
@@ -170,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -178,9 +178,9 @@
     "offset=0\n",
     "max_difference=0.02\n",
     "\n",
-    "first_file = \"/mnt/d/VMware_share/Data/TUM/rgbd_dataset_freiburg3_walking_xyz/groundtruth.txt\"\n",
-    "orb_file =  \"/mnt/d/VMware_share/Experiments/trajectories/w_xyz_ORB.txt\"\n",
-    "dyna_file = \"/mnt/d/VMware_share/Experiments/trajectories/w_xyz_Dyna1.txt\"\n",
+    "first_file = \"/mnt/d/VMware_share/Data/TUM/rgbd_dataset_freiburg3_walking_static/groundtruth.txt\"\n",
+    "orb_file =  \"/mnt/d/VMware_share/Experiments/trajectories/w_static_ORB.txt\"\n",
+    "dyna_file = \"/mnt/d/VMware_share/Experiments/trajectories/w_static_Dyna1.txt\"\n",
     "\n",
     "first_list = associate.read_file_list(first_file)\n",
     "first_stamps = first_list.keys()\n",
@@ -218,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -227,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -236,16 +236,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.6415009082507123, 0.016072188217995463)"
+       "(0.37886774311630056, 0.01611563688767199)"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 128,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -256,12 +256,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -304,41 +304,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [],
    "source": [
-    "ground_truth = \"/mnt/d/VMware_share/Data/TUM/rgbd_dataset_freiburg3_walking_halfsphere/groundtruth.txt\"\n",
-    "file_name = \"/mnt/d/VMware_share/Experiments/trajectories/w_half_Dyna{}.txt\""
+    "ground_truth = \"/mnt/d/VMware_share/Data/TUM/rgbd_dataset_freiburg3_walking_rpy/groundtruth.txt\"\n",
+    "file_name = \"/mnt/d/VMware_share/Experiments/trajectories/w_rpy_Dyna{}.txt\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 120,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "IOError",
-     "evalue": "[Errno 2] No such file or directory: '/mnt/d/VMware_share/Experiments/trajectories/w_half_Dyna0.txt'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-51-04ce2da1ca6e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0merrors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcalc_errors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mground_truth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-33-c3fdadd80f4e>\u001b[0m in \u001b[0;36mcalc_errors\u001b[0;34m(ground_truth, filename)\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0merrors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m         \u001b[0merror\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcalculate_traj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mground_truth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m         \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-3-1e9f389a8222>\u001b[0m in \u001b[0;36mcalculate_traj\u001b[0;34m(first_file, second_file)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mfirst_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0massociate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_file_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfirst_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0msecond_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0massociate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_file_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msecond_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/mnt/d/VMware_share/Projects/Dynamic_ORB_SLAM2/python/rgbd_benchmark_tools/associate.py\u001b[0m in \u001b[0;36mread_file_list\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \"\"\"\n\u001b[0;32m---> 64\u001b[0;31m     \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m     \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\",\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\t\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: '/mnt/d/VMware_share/Experiments/trajectories/w_half_Dyna0.txt'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "errors = calc_errors(ground_truth, file_name)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
@@ -355,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
@@ -364,7 +349,7 @@
        "0.0704165025406468"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -375,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
@@ -384,7 +369,7 @@
        "0.1513750157266809"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -395,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
@@ -404,7 +389,7 @@
        "0.12300678746662759"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/results/s_half_Dyna0.txt b/results/TUM_rgbd/s_half_Dyna0.txt
similarity index 100%
rename from results/s_half_Dyna0.txt
rename to results/TUM_rgbd/s_half_Dyna0.txt
diff --git a/results/s_half_Dyna1.txt b/results/TUM_rgbd/s_half_Dyna1.txt
similarity index 100%
rename from results/s_half_Dyna1.txt
rename to results/TUM_rgbd/s_half_Dyna1.txt
diff --git a/results/s_half_Dyna2.txt b/results/TUM_rgbd/s_half_Dyna2.txt
similarity index 100%
rename from results/s_half_Dyna2.txt
rename to results/TUM_rgbd/s_half_Dyna2.txt
diff --git a/results/s_half_Dyna3.txt b/results/TUM_rgbd/s_half_Dyna3.txt
similarity index 100%
rename from results/s_half_Dyna3.txt
rename to results/TUM_rgbd/s_half_Dyna3.txt
diff --git a/results/s_half_Dyna4.txt b/results/TUM_rgbd/s_half_Dyna4.txt
similarity index 100%
rename from results/s_half_Dyna4.txt
rename to results/TUM_rgbd/s_half_Dyna4.txt
diff --git a/results/s_rpy_Dyna0.txt b/results/TUM_rgbd/s_rpy_Dyna0.txt
similarity index 100%
rename from results/s_rpy_Dyna0.txt
rename to results/TUM_rgbd/s_rpy_Dyna0.txt
diff --git a/results/s_rpy_Dyna1.txt b/results/TUM_rgbd/s_rpy_Dyna1.txt
similarity index 100%
rename from results/s_rpy_Dyna1.txt
rename to results/TUM_rgbd/s_rpy_Dyna1.txt
diff --git a/results/s_rpy_Dyna2.txt b/results/TUM_rgbd/s_rpy_Dyna2.txt
similarity index 100%
rename from results/s_rpy_Dyna2.txt
rename to results/TUM_rgbd/s_rpy_Dyna2.txt
diff --git a/results/s_rpy_Dyna3.txt b/results/TUM_rgbd/s_rpy_Dyna3.txt
similarity index 100%
rename from results/s_rpy_Dyna3.txt
rename to results/TUM_rgbd/s_rpy_Dyna3.txt
diff --git a/results/s_rpy_Dyna4.txt b/results/TUM_rgbd/s_rpy_Dyna4.txt
similarity index 100%
rename from results/s_rpy_Dyna4.txt
rename to results/TUM_rgbd/s_rpy_Dyna4.txt
diff --git a/results/s_rpy_ORB.txt b/results/TUM_rgbd/s_rpy_ORB.txt
similarity index 100%
rename from results/s_rpy_ORB.txt
rename to results/TUM_rgbd/s_rpy_ORB.txt
diff --git a/results/s_static_Dyna0.txt b/results/TUM_rgbd/s_static_Dyna0.txt
similarity index 100%
rename from results/s_static_Dyna0.txt
rename to results/TUM_rgbd/s_static_Dyna0.txt
diff --git a/results/s_static_Dyna1.txt b/results/TUM_rgbd/s_static_Dyna1.txt
similarity index 100%
rename from results/s_static_Dyna1.txt
rename to results/TUM_rgbd/s_static_Dyna1.txt
diff --git a/results/s_static_Dyna2.txt b/results/TUM_rgbd/s_static_Dyna2.txt
similarity index 100%
rename from results/s_static_Dyna2.txt
rename to results/TUM_rgbd/s_static_Dyna2.txt
diff --git a/results/s_static_Dyna3.txt b/results/TUM_rgbd/s_static_Dyna3.txt
similarity index 100%
rename from results/s_static_Dyna3.txt
rename to results/TUM_rgbd/s_static_Dyna3.txt
diff --git a/results/s_static_Dyna4.txt b/results/TUM_rgbd/s_static_Dyna4.txt
similarity index 100%
rename from results/s_static_Dyna4.txt
rename to results/TUM_rgbd/s_static_Dyna4.txt
diff --git a/results/s_xyz_Dyna0.txt b/results/TUM_rgbd/s_xyz_Dyna0.txt
similarity index 100%
rename from results/s_xyz_Dyna0.txt
rename to results/TUM_rgbd/s_xyz_Dyna0.txt
diff --git a/results/s_xyz_Dyna1.txt b/results/TUM_rgbd/s_xyz_Dyna1.txt
similarity index 100%
rename from results/s_xyz_Dyna1.txt
rename to results/TUM_rgbd/s_xyz_Dyna1.txt
diff --git a/results/s_xyz_Dyna2.txt b/results/TUM_rgbd/s_xyz_Dyna2.txt
similarity index 100%
rename from results/s_xyz_Dyna2.txt
rename to results/TUM_rgbd/s_xyz_Dyna2.txt
diff --git a/results/s_xyz_Dyna3.txt b/results/TUM_rgbd/s_xyz_Dyna3.txt
similarity index 100%
rename from results/s_xyz_Dyna3.txt
rename to results/TUM_rgbd/s_xyz_Dyna3.txt
diff --git a/results/s_xyz_Dyna4.txt b/results/TUM_rgbd/s_xyz_Dyna4.txt
similarity index 100%
rename from results/s_xyz_Dyna4.txt
rename to results/TUM_rgbd/s_xyz_Dyna4.txt
diff --git a/results/w_rpy_Dyna0.txt b/results/TUM_rgbd/w_rpy_Dyna0.txt
similarity index 100%
rename from results/w_rpy_Dyna0.txt
rename to results/TUM_rgbd/w_rpy_Dyna0.txt
diff --git a/results/w_rpy_Dyna1.txt b/results/TUM_rgbd/w_rpy_Dyna1.txt
similarity index 100%
rename from results/w_rpy_Dyna1.txt
rename to results/TUM_rgbd/w_rpy_Dyna1.txt
diff --git a/results/w_rpy_Dyna2.txt b/results/TUM_rgbd/w_rpy_Dyna2.txt
similarity index 100%
rename from results/w_rpy_Dyna2.txt
rename to results/TUM_rgbd/w_rpy_Dyna2.txt
diff --git a/results/w_rpy_Dyna3.txt b/results/TUM_rgbd/w_rpy_Dyna3.txt
similarity index 100%
rename from results/w_rpy_Dyna3.txt
rename to results/TUM_rgbd/w_rpy_Dyna3.txt
diff --git a/results/w_rpy_Dyna4.txt b/results/TUM_rgbd/w_rpy_Dyna4.txt
similarity index 100%
rename from results/w_rpy_Dyna4.txt
rename to results/TUM_rgbd/w_rpy_Dyna4.txt
diff --git a/results/w_static_Dyna0.txt b/results/TUM_rgbd/w_static_Dyna0.txt
similarity index 100%
rename from results/w_static_Dyna0.txt
rename to results/TUM_rgbd/w_static_Dyna0.txt
diff --git a/results/w_static_Dyna1.txt b/results/TUM_rgbd/w_static_Dyna1.txt
similarity index 100%
rename from results/w_static_Dyna1.txt
rename to results/TUM_rgbd/w_static_Dyna1.txt
diff --git a/results/w_static_Dyna2.txt b/results/TUM_rgbd/w_static_Dyna2.txt
similarity index 100%
rename from results/w_static_Dyna2.txt
rename to results/TUM_rgbd/w_static_Dyna2.txt
diff --git a/results/w_static_Dyna3.txt b/results/TUM_rgbd/w_static_Dyna3.txt
similarity index 100%
rename from results/w_static_Dyna3.txt
rename to results/TUM_rgbd/w_static_Dyna3.txt
diff --git a/results/w_static_Dyna4.txt b/results/TUM_rgbd/w_static_Dyna4.txt
similarity index 100%
rename from results/w_static_Dyna4.txt
rename to results/TUM_rgbd/w_static_Dyna4.txt
diff --git a/results/w_static_ORB.txt b/results/TUM_rgbd/w_static_ORB.txt
similarity index 100%
rename from results/w_static_ORB.txt
rename to results/TUM_rgbd/w_static_ORB.txt
diff --git a/results/w_xyz_Dyna0.txt b/results/TUM_rgbd/w_xyz_Dyna0.txt
similarity index 100%
rename from results/w_xyz_Dyna0.txt
rename to results/TUM_rgbd/w_xyz_Dyna0.txt
diff --git a/results/w_xyz_Dyna1.txt b/results/TUM_rgbd/w_xyz_Dyna1.txt
similarity index 100%
rename from results/w_xyz_Dyna1.txt
rename to results/TUM_rgbd/w_xyz_Dyna1.txt
diff --git a/results/w_xyz_Dyna2.txt b/results/TUM_rgbd/w_xyz_Dyna2.txt
similarity index 100%
rename from results/w_xyz_Dyna2.txt
rename to results/TUM_rgbd/w_xyz_Dyna2.txt
diff --git a/results/w_xyz_Dyna3.txt b/results/TUM_rgbd/w_xyz_Dyna3.txt
similarity index 100%
rename from results/w_xyz_Dyna3.txt
rename to results/TUM_rgbd/w_xyz_Dyna3.txt
diff --git a/results/w_xyz_Dyna4.txt b/results/TUM_rgbd/w_xyz_Dyna4.txt
similarity index 100%
rename from results/w_xyz_Dyna4.txt
rename to results/TUM_rgbd/w_xyz_Dyna4.txt
diff --git a/results/x_half_Dyna0.txt b/results/TUM_rgbd/x_half_Dyna0.txt
similarity index 100%
rename from results/x_half_Dyna0.txt
rename to results/TUM_rgbd/x_half_Dyna0.txt
diff --git a/results/x_half_Dyna1.txt b/results/TUM_rgbd/x_half_Dyna1.txt
similarity index 100%
rename from results/x_half_Dyna1.txt
rename to results/TUM_rgbd/x_half_Dyna1.txt
diff --git a/results/x_half_Dyna2.txt b/results/TUM_rgbd/x_half_Dyna2.txt
similarity index 100%
rename from results/x_half_Dyna2.txt
rename to results/TUM_rgbd/x_half_Dyna2.txt
diff --git a/results/x_half_Dyna3.txt b/results/TUM_rgbd/x_half_Dyna3.txt
similarity index 100%
rename from results/x_half_Dyna3.txt
rename to results/TUM_rgbd/x_half_Dyna3.txt
diff --git a/results/x_half_Dyna4.txt b/results/TUM_rgbd/x_half_Dyna4.txt
similarity index 100%
rename from results/x_half_Dyna4.txt
rename to results/TUM_rgbd/x_half_Dyna4.txt