Ros2 v0.8.0 lidar apollo instance segmentation (autowarefoundation#256)

* restore file name for v0.8.0 update Signed-off-by: wep21 <border_goldenmarket@yahoo.co.jp> * fix typos in perception (autowarefoundation#862) * Revert "restore file name for v0.8.0 update" This reverts commit f8e44568acf0613a0385c9323f2d25bcf45025d2. Co-authored-by: Kazuki Miyahara <kmiya@outlook.com>
kosuke55 · Jan 30, 2021 · 1937501 · 1937501
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diff --git a/...ption/object_recognition/detection/lidar_apollo_instance_segmentation/README.md b/...ption/object_recognition/detection/lidar_apollo_instance_segmentation/README.md
@@ -15,7 +15,7 @@ Original URL
 
 Supported lidars are velodyne 16, 64 and 128, but you can also use velodyne 32 and other lidars with good accuracy.
 
-1. [apollo 3D Obstacle Percption description](https://github.com/ApolloAuto/apollo/blob/master/docs/specs/3d_obstacle_perception.md)
+1. [apollo 3D Obstacle Perception description](https://github.com/ApolloAuto/apollo/blob/master/docs/specs/3d_obstacle_perception.md)
 ```
 /******************************************************************************
  * Copyright 2017 The Apollo Authors. All Rights Reserved.

diff --git a/...r_apollo_instance_segmentation/include/lidar_apollo_instance_segmentation/feature_map.hpp b/...r_apollo_instance_segmentation/include/lidar_apollo_instance_segmentation/feature_map.hpp
@@ -22,14 +22,14 @@ struct FeatureMapInterface
   int width;
   int height;
   int range;
-  float * max_height_data;      // channnel 0
-  float * mean_height_data;     // channnel 1
-  float * count_data;           // channnel 2
-  float * direction_data;       // channnel 3
-  float * top_intensity_data;   // channnel 4
-  float * mean_intensity_data;  // channnel 5
-  float * distance_data;        // channnel 6
-  float * nonempty_data;        // channnel 7
+  float * max_height_data;      // channel 0
+  float * mean_height_data;     // channel 1
+  float * count_data;           // channel 2
+  float * direction_data;       // channel 3
+  float * top_intensity_data;   // channel 4
+  float * mean_intensity_data;  // channel 5
+  float * distance_data;        // channel 6
+  float * nonempty_data;        // channel 7
   std::vector<float> map_data;
   virtual void initializeMap(std::vector<float> & map) = 0;
   virtual void resetMap(std::vector<float> & map) = 0;

diff --git a/...on/lidar_apollo_instance_segmentation/include/lidar_apollo_instance_segmentation/util.hpp b/...on/lidar_apollo_instance_segmentation/include/lidar_apollo_instance_segmentation/util.hpp
@@ -34,7 +34,7 @@
 #include <cmath>
 #include <string>
 
-// project point cloud to 2d map. clac in which grid point is.
+// project point cloud to 2d map. calc in which grid point is.
 // pointcloud to pixel
 inline int F2I(float val, float ori, float scale)
 {
@@ -47,7 +47,7 @@ inline int Pc2Pixel(float in_pc, float in_range, float out_size)
   return static_cast<int>(std::floor((in_range - in_pc) * inv_res));
 }
 
-// retutn the distance from my car to center of the grid.
+// return the distance from my car to center of the grid.
 // Pc means point cloud = real world scale. so transform pixel scale to real
 // world scale
 inline float Pixel2Pc(int in_pixel, float in_size, float out_range)

diff --git a/perception/object_recognition/detection/lidar_apollo_instance_segmentation/src/detector.cpp b/perception/object_recognition/detection/lidar_apollo_instance_segmentation/src/detector.cpp
@@ -94,7 +94,7 @@ bool LidarApolloInstanceSegmentation::transformCloud(
   pcl::PointCloud<pcl::PointXYZI> pcl_input, pcl_transformed_cloud;
   pcl::fromROSMsg(input, pcl_input);
 
-  // transform pointcloud to tagret_frame
+  // transform pointcloud to target_frame
   if (target_frame_ != input.header.frame_id) {
     try {
       geometry_msgs::msg::TransformStamped transform_stamped;

diff --git a/...ption/object_recognition/detection/lidar_apollo_instance_segmentation/src/feature_map.cpp b/...ption/object_recognition/detection/lidar_apollo_instance_segmentation/src/feature_map.cpp
@@ -94,12 +94,12 @@ void FeatureMapWithConstant::initializeMap(std::vector<float> & map)
     for (int col = 0; col < width; ++col) {
       int idx = row * width + col;
       // * row <-> x, column <-> y
-      // retutn the distance from my car to center of the grid.
+      // return the distance from my car to center of the grid.
       // Pc means point cloud = real world scale. so transform pixel scale to
       // real world scale
       float center_x = Pixel2Pc(row, height, range);
       float center_y = Pixel2Pc(col, width, range);
-      // normaliztion. -0.5~0.5
+      // normalization. -0.5~0.5
       direction_data[idx] = static_cast<float>(std::atan2(center_y, center_x) / (2.0 * M_PI));
       distance_data[idx] = static_cast<float>(std::hypot(center_x, center_y) / 60.0 - 0.5);
     }
@@ -136,12 +136,12 @@ void FeatureMapWithConstantAndIntensity::initializeMap(std::vector<float> & map)
     for (int col = 0; col < width; ++col) {
       int idx = row * width + col;
       // * row <-> x, column <-> y
-      // retutn the distance from my car to center of the grid.
+      // return the distance from my car to center of the grid.
       // Pc means point cloud = real world scale. so transform pixel scale to
       // real world scale
       float center_x = Pixel2Pc(row, height, range);
       float center_y = Pixel2Pc(col, width, range);
-      // normaliztion. -0.5~0.5
+      // normalization. -0.5~0.5
       direction_data[idx] = static_cast<float>(std::atan2(center_y, center_x) / (2.0 * M_PI));
       distance_data[idx] = static_cast<float>(std::hypot(center_x, center_y) / 60.0 - 0.5);
     }