increased the confidence threshold of the YOLO model to prevent overl…

…apping labels and prevented navigator from investigating large objects when running in the simulation evenironment
uf-mil · Sep 6, 2024 · 01bbfbb · 01bbfbb
1 parent 20c1958
commit 01bbfbb
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Showing 2 changed files with 33 additions and 2 deletions.
diff --git a/NaviGator/mission_control/navigator_launch/launch/perception/classifier.launch b/NaviGator/mission_control/navigator_launch/launch/perception/classifier.launch
@@ -13,7 +13,7 @@
   <arg name="out_topic_model1" default="detections_model1" />
   <arg name="out_topic_model2" default="detections_model2" />
   <arg name="out_topic_model3" default="stc_detections_model" />
-  <arg name="conf_thresh" default="0.25" />
+  <arg name="conf_thresh" default="0.7" />
   <arg name="image_size" default="640" />
   <arg name="device" default="cpu" />
   <arg name="use_yolo_model1" default="False" />

diff --git a/NaviGator/mission_control/navigator_missions/navigator_missions/navigation.py b/NaviGator/mission_control/navigator_missions/navigator_missions/navigation.py
@@ -290,10 +290,26 @@ async def explore_closest_until(self, is_done, filter_and_sort):
             # and find closest one within 25 meters (max width of a gate).
             if cone_buoys_investigated > 0 and potential_candidate is None:
                 for i in range(len(objects)):
-                    print(positions[i])
+                    obj_vol = (
+                        objects[i].scale.x * objects[i].scale.y * objects[i].scale.z
+                    )
+
+                    # Catch case where we invesitegate the shore
+                    if obj_vol > 3:
+                        print(
+                            f"Object {i} volume: ",
+                            objects[i].scale.x
+                            * objects[i].scale.y
+                            * objects[i].scale.z,
+                        )
+                        continue
+                    else:
+                        print(positions[i])
+
                     if (
                         objects[i].id not in investigated
                         and "round" not in objects[i].labeled_classification
+                        and obj_vol < 3
                     ):
                         distance = np.linalg.norm(positions[i] - self.pose[0])
                         if distance < shortest_distance and distance <= 25:
@@ -308,9 +324,24 @@ async def explore_closest_until(self, is_done, filter_and_sort):
             # if that doesn't produce any results, literally just go to closest buoy
             if potential_candidate is None:
                 for i in range(len(objects)):
+                    obj_vol = (
+                        objects[i].scale.x * objects[i].scale.y * objects[i].scale.z
+                    )
+
+                    # Catch case where we invesitegate the shore
+                    if obj_vol > 3:
+                        print(
+                            f"Object {i} volume: ",
+                            objects[i].scale.x
+                            * objects[i].scale.y
+                            * objects[i].scale.z,
+                        )
+                        continue
+
                     if (
                         objects[i].id not in investigated
                         and "round" not in objects[i].labeled_classification
+                        and obj_vol < 3
                     ):
                         distance = np.linalg.norm(positions[i] - self.pose[0])
                         if distance < shortest_distance: