fix(image_projection_based_fusion): segmentation pointcloud fusion pa…

…ram update (autowarefoundation#7858)

perception/image_projection_based_fusion/config/segmentation_pointcloud_fusion.param.yaml

@@ -1,39 +1,24 @@
 /**:
   ros__parameters:
     # if the semantic label is applied for pointcloud filtering
+
     filter_semantic_label_target:
-      [
-      true, # road
-      true, # sidewalk
-      true, # building
-      true, # wall
-      true, # fence
-      true, # pole
-      true, # traffic_light
-      true, # traffic_sign
-      true, # vegetation
-      true, # terrain
-      true, # sky
-      false, # person
-      false, # ride
-      false, # car
-      false, # truck
-      false, # bus
-      false, # train
-      false, # motorcycle
-      false, # bicycle
-      false, # others
-      ]
-    # the maximum distance of pointcloud to be applied filter,
-    # this is selected based on semantic segmentation model accuracy,
-    # calibration accuracy and unknown reaction distance
-    filter_distance_threshold: 60.0
+      UNKNOWN: false
+      BUILDING: true
+      WALL: true
+      OBSTACLE: false
+      TRAFFIC_LIGHT: false
+      TRAFFIC_SIGN: false
+      PERSON: false
+      VEHICLE: false
+      BIKE: false
+      ROAD: true
+      SIDEWALK: false
+      ROAD_PAINT: false
+      CURBSTONE: false
+      CROSSWALK: false
+      VEGETATION: true
+      SKY: false
 
-    # debug
-    debug_mode: false
-    filter_scope_min_x: -100.0
-    filter_scope_max_x: 100.0
-    filter_scope_min_y: -100.0
-    filter_scope_max_y: 100.0
-    filter_scope_min_z: -100.0
-    filter_scope_max_z: 100.0
+    # the maximum distance of pointcloud to be applied filter
+    filter_distance_threshold: 60.0

perception/image_projection_based_fusion/docs/segmentation-pointcloud-fusion.md

@@ -32,9 +32,7 @@ The node `segmentation_pointcloud_fusion` is a package for filtering pointcloud
 
 ### Core Parameters
 
-| Name          | Type | Description              |
-| ------------- | ---- | ------------------------ |
-| `rois_number` | int  | the number of input rois |
+{{ json_to_markdown("perception/image_projection_based_fusion/schema/segmentation_pointcloud_fusion.schema.json") }}
 
 ## Assumptions / Known limits
 

perception/image_projection_based_fusion/include/image_projection_based_fusion/segmentation_pointcloud_fusion/node.hpp

@@ -18,6 +18,7 @@
 #include "image_projection_based_fusion/fusion_node.hpp"
 
 #include <string>
+#include <utility>
 #include <vector>
 
 #if __has_include(<cv_bridge/cv_bridge.hpp>)
@@ -34,7 +35,13 @@ class SegmentPointCloudFusionNode : public FusionNode<PointCloud2, PointCloud2,
   rclcpp::Publisher<PointCloud2>::SharedPtr pub_pointcloud_ptr_;
   std::vector<bool> filter_semantic_label_target_;
   float filter_distance_threshold_;
-  /* data */
+  // declare list of semantic label target, depend on trained data of yolox segmentation model
+  std::vector<std::pair<std::string, bool>> filter_semantic_label_target_list_ = {
+    {"UNKNOWN", false},       {"BUILDING", false},     {"WALL", false},       {"OBSTACLE", false},
+    {"TRAFFIC_LIGHT", false}, {"TRAFFIC_SIGN", false}, {"PERSON", false},     {"VEHICLE", false},
+    {"BIKE", false},          {"ROAD", false},         {"SIDEWALK", false},   {"ROAD_PAINT", false},
+    {"CURBSTONE", false},     {"CROSSWALK", false},    {"VEGETATION", false}, {"SKY", false}};
+
 public:
   explicit SegmentPointCloudFusionNode(const rclcpp::NodeOptions & options);
 

perception/image_projection_based_fusion/schema/segmentation_pointcloud_fusion.schema.json

@@ -0,0 +1,134 @@
+{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "title": "Parameters for Segmentation Pointcloud Fusion Node",
+  "type": "object",
+  "definitions": {
+    "segmentation_pointcloud_fusion": {
+      "type": "object",
+      "properties": {
+        "filter_semantic_label_target": {
+          "type": "object",
+          "properties": {
+            "UNKNOWN": {
+              "type": "boolean",
+              "description": "If true, UNKNOWN class of semantic will be filtered.",
+              "default": false
+            },
+            "BUILDING": {
+              "type": "boolean",
+              "description": "If true, BUILDING class of semantic will be filtered.",
+              "default": true
+            },
+            "WALL": {
+              "type": "boolean",
+              "description": "If true, WALL class of semantic will be filtered.",
+              "default": true
+            },
+            "OBSTACLE": {
+              "type": "boolean",
+              "description": "If true, OBSTACLE class of semantic will be filtered.",
+              "default": false
+            },
+            "TRAFFIC_LIGHT": {
+              "type": "boolean",
+              "description": "If true, TRAFFIC_LIGHT class of semantic will be filtered.",
+              "default": false
+            },
+            "TRAFFIC_SIGN": {
+              "type": "boolean",
+              "description": "If true, TRAFFIC_SIGN class of semantic will be filtered.",
+              "default": false
+            },
+            "PERSON": {
+              "type": "boolean",
+              "description": "If true, PERSON class of semantic will be filtered.",
+              "default": false
+            },
+            "VEHICLE": {
+              "type": "boolean",
+              "description": "If true, VEHICLE class of semantic will be filtered.",
+              "default": false
+            },
+            "BIKE": {
+              "type": "boolean",
+              "description": "If true, BIKE class of semantic will be filtered.",
+              "default": false
+            },
+            "ROAD": {
+              "type": "boolean",
+              "description": "If true, ROAD class of semantic will be filtered.",
+              "default": true
+            },
+            "SIDEWALK": {
+              "type": "boolean",
+              "description": "If true, SIDEWALK class of semantic will be filtered.",
+              "default": false
+            },
+            "ROAD_PAINT": {
+              "type": "boolean",
+              "description": "If true, ROAD_PAINT class of semantic will be filtered.",
+              "default": false
+            },
+            "CURBSTONE": {
+              "type": "boolean",
+              "description": "If true, CURBSTONE class of semantic will be filtered.",
+              "default": false
+            },
+            "CROSSWALK": {
+              "type": "boolean",
+              "description": "If true, CROSSWALK class of semantic will be filtered.",
+              "default": false
+            },
+            "VEGETATION": {
+              "type": "boolean",
+              "description": "If true, VEGETATION class of semantic will be filtered.",
+              "default": true
+            },
+            "SKY": {
+              "type": "boolean",
+              "description": "If true, SKY class of semantic will be filtered.",
+              "default": false
+            }
+          },
+          "required": [
+            "UNKNOWN",
+            "BUILDING",
+            "WALL",
+            "OBSTACLE",
+            "TRAFFIC_LIGHT",
+            "TRAFFIC_SIGN",
+            "PERSON",
+            "VEHICLE",
+            "BIKE",
+            "ROAD",
+            "SIDEWALK",
+            "ROAD_PAINT",
+            "CURBSTONE",
+            "CROSSWALK",
+            "VEGETATION",
+            "SKY"
+          ]
+        },
+        "filter_distance_threshold": {
+          "type": "number",
+          "description": "A maximum distance of pointcloud to apply filter [m].",
+          "default": 60.0,
+          "minimum": 0.0
+        }
+      },
+      "required": ["filter_semantic_label_target", "filter_distance_threshold"]
+    }
+  },
+  "properties": {
+    "/**": {
+      "type": "object",
+      "properties": {
+        "ros__parameters": {
+          "$ref": "#/definitions/segmentation_pointcloud_fusion"
+        }
+      },
+      "required": ["ros__parameters"]
+    }
+  },
+  "required": ["/**"]
+}

perception/image_projection_based_fusion/src/segmentation_pointcloud_fusion/node.cpp

@@ -31,8 +31,13 @@ SegmentPointCloudFusionNode::SegmentPointCloudFusionNode(const rclcpp::NodeOptio
 : FusionNode<PointCloud2, PointCloud2, Image>("segmentation_pointcloud_fusion", options)
 {
   filter_distance_threshold_ = declare_parameter<float>("filter_distance_threshold");
-  filter_semantic_label_target_ =
-    declare_parameter<std::vector<bool>>("filter_semantic_label_target");
+  for (auto & item : filter_semantic_label_target_list_) {
+    item.second = declare_parameter<bool>("filter_semantic_label_target." + item.first);
+  }
+  for (const auto & item : filter_semantic_label_target_list_) {
+    RCLCPP_INFO(
+      this->get_logger(), "filter_semantic_label_target: %s %d", item.first.c_str(), item.second);
+  }
 }
 
 void SegmentPointCloudFusionNode::preprocess(__attribute__((unused)) PointCloud2 & pointcloud_msg)
@@ -135,12 +140,12 @@ void SegmentPointCloudFusionNode::fuseOnSingleImage(
     uint8_t semantic_id = mask.at<uint8_t>(
       static_cast<uint16_t>(normalized_projected_point.y()),
       static_cast<uint16_t>(normalized_projected_point.x()));
-    if (static_cast<size_t>(semantic_id) >= filter_semantic_label_target_.size()) {
+    if (static_cast<size_t>(semantic_id) >= filter_semantic_label_target_list_.size()) {
       copyPointCloud(
         input_pointcloud_msg, point_step, global_offset, output_cloud, output_pointcloud_size);
       continue;
     }
-    if (!filter_semantic_label_target_.at(semantic_id)) {
+    if (!filter_semantic_label_target_list_.at(semantic_id).second) {
       copyPointCloud(
         input_pointcloud_msg, point_step, global_offset, output_cloud, output_pointcloud_size);
     }