refactor: fix spell-check (autowarefoundation#4289)

* refactor: fix spell-check

Signed-off-by: Shunsuke Miura <shunsuke.miura@tier4.jp>

* fix spell-check

Signed-off-by: Shunsuke Miura <shunsuke.miura@tier4.jp>

* fix typo

Signed-off-by: Shunsuke Miura <shunsuke.miura@tier4.jp>

* Fix obvious typo, shortened words

Signed-off-by: Shunsuke Miura <shunsuke.miura@tier4.jp>

* Fix obvious typo, shortened words, in common directory

Signed-off-by: Shunsuke Miura <shunsuke.miura@tier4.jp>

* add cspell ignore

Signed-off-by: Shunsuke Miura <shunsuke.miura@tier4.jp>

* Update perception/tensorrt_classifier/CMakeLists.txt

Co-authored-by: Yusuke Muramatsu <yukke42@users.noreply.github.com>

---------

Signed-off-by: Shunsuke Miura <shunsuke.miura@tier4.jp>
Co-authored-by: Yusuke Muramatsu <yukke42@users.noreply.github.com>

Author: miursh

common/perception_utils/include/perception_utils/prime_synchronizer.hpp

@@ -158,7 +158,7 @@ class PrimeSynchronizer
    * @tparam Idx
    * @param argv
    * @return true  All messages are not nullptr
-   * @return false At least one message in the tuplc is nullptr
+   * @return false At least one message in the topic is nullptr
    */
   template <std::size_t Idx = 0>
   bool isArgvValid(

common/tensorrt_common/src/tensorrt_common.cpp

@@ -416,6 +416,7 @@ bool TrtCommon::buildEngineFromOnnx(
           first = false;
         }
         if (last) {
+          // cspell: ignore preds
           if (
             contain(name, "reg_preds") || contain(name, "cls_preds") ||
             contain(name, "obj_preds")) {

common/tvm_utility/tvm_utility-extras.cmake

@@ -35,6 +35,7 @@ function(get_neural_network MODEL_NAME MODEL_BACKEND DEPENDENCY)
   set(PREPROCESSING "")
 
   # Prioritize user-provided models.
+  # cspell: ignore COPYONLY
   if(IS_DIRECTORY "${DATA_PATH}/user/${MODEL_NAME}")
     message(STATUS "Using user-provided model from ${DATA_PATH}/user/${MODEL_NAME}")
     file(REMOVE_RECURSE "${DATA_PATH}/models/${MODEL_NAME}/")

perception/ground_segmentation/test/test_ray_ground_filter.cpp

@@ -109,13 +109,13 @@ TEST_F(RayGroundFilterComponentTestSuite, TestCase1)
   // check out_cloud
   int effect_num = 0;
   int total_num = 0;
-  const float min_noground_point_z = 0.1;  // z in base_frame
+  const float min_no_ground_point_z = 0.1;  // z in base_frame
   for (sensor_msgs::PointCloud2ConstIterator<float> iter_x(out_cloud, "x"), iter_y(out_cloud, "y"),
        iter_z(out_cloud, "z");
        iter_x != iter_x.end(); ++iter_x, ++iter_y, ++iter_z) {
     const float z = *iter_z;
     total_num += 1;
-    if (z > min_noground_point_z) {
+    if (z > min_no_ground_point_z) {
       effect_num += 1;
     }
   }

perception/ground_segmentation/test/test_scan_ground_filter.cpp

@@ -45,7 +45,7 @@ class ScanGroundFilterTest : public ::testing::Test
     output_pointcloud_pub_ = rclcpp::create_publisher<sensor_msgs::msg::PointCloud2>(
       dummy_node_, "/test_scan_ground_filter/output_cloud", 1);
 
-    // no real uages,ScanGroundFilterComponent cosntruct need these params
+    // no real usages, ScanGroundFilterComponent constructor need these params
     rclcpp::NodeOptions options;
     std::vector<rclcpp::Parameter> parameters;
     parameters.emplace_back(rclcpp::Parameter("wheel_radius", 0.39));
@@ -173,13 +173,13 @@ TEST_F(ScanGroundFilterTest, TestCase1)
   // check out_cloud
   int effect_num = 0;
   int total_num = 0;
-  const float min_noground_point_z = 0.1;
+  const float min_no_ground_point_z = 0.1;
   for (sensor_msgs::PointCloud2ConstIterator<float> iter_x(out_cloud, "x"), iter_y(out_cloud, "y"),
        iter_z(out_cloud, "z");
        iter_x != iter_x.end(); ++iter_x, ++iter_y, ++iter_z) {
     const float z = *iter_z;
     total_num += 1;
-    if (z > min_noground_point_z) {
+    if (z > min_no_ground_point_z) {
       effect_num += 1;
     }
   }

perception/lidar_centerpoint_tvm/lib/postprocess/generate_detected_boxes.cpp

@@ -146,28 +146,28 @@ void generateDetectedBoxes3D(
     // construct boxes3d failed
     std::cerr << "lidar_centerpoint_tvm: construct boxes3d failed" << std::endl;
   }
-  std::vector<Box3D> det_boxes3d_nonms(num_det_boxes3d);
+  std::vector<Box3D> det_boxes3d_no_nms(num_det_boxes3d);
   std::copy_if(
-    boxes3d.begin(), boxes3d.end(), det_boxes3d_nonms.begin(),
+    boxes3d.begin(), boxes3d.end(), det_boxes3d_no_nms.begin(),
     is_score_greater(config.score_threshold_));
 
   // sort by score
-  std::sort(det_boxes3d_nonms.begin(), det_boxes3d_nonms.end(), score_greater());
+  std::sort(det_boxes3d_no_nms.begin(), det_boxes3d_no_nms.end(), score_greater());
 
   // suppress by NMS
   std::vector<bool> final_keep_mask(num_det_boxes3d);
   const auto num_final_det_boxes3d =
-    circleNMS(det_boxes3d_nonms, config.circle_nms_dist_threshold_, final_keep_mask);
+    circleNMS(det_boxes3d_no_nms, config.circle_nms_dist_threshold_, final_keep_mask);
 
   det_boxes3d.resize(num_final_det_boxes3d);
   std::size_t box_id = 0;
   for (std::size_t idx = 0; idx < final_keep_mask.size(); idx++) {
     if (final_keep_mask[idx]) {
-      det_boxes3d[box_id] = det_boxes3d_nonms[idx];
+      det_boxes3d[box_id] = det_boxes3d_no_nms[idx];
       box_id++;
     }
   }
-  // std::copy_if(det_boxes3d_nonms.begin(), det_boxes3d_nonms.end(), final_keep_mask.begin(),
+  // std::copy_if(det_boxes3d_no_nms.begin(), det_boxes3d_no_nms.end(), final_keep_mask.begin(),
   //   det_boxes3d.begin(), is_kept());
 }
 

perception/lidar_centerpoint_tvm/lib/preprocess/voxel_generator.cpp

@@ -75,7 +75,7 @@ std::size_t VoxelGenerator::pointsToVoxels(
     auto pc_msg = pc_cache_iter->pointcloud_msg;
     auto affine_past2current =
       pd_ptr_->getAffineWorldToCurrent() * pc_cache_iter->affine_past2world;
-    float timelag = static_cast<float>(
+    float time_lag = static_cast<float>(
       pd_ptr_->getCurrentTimestamp() - rclcpp::Time(pc_msg.header.stamp).seconds());
 
     for (sensor_msgs::PointCloud2ConstIterator<float> x_iter(pc_msg, "x"), y_iter(pc_msg, "y"),
@@ -87,7 +87,7 @@ std::size_t VoxelGenerator::pointsToVoxels(
       point[0] = point_current.x();
       point[1] = point_current.y();
       point[2] = point_current.z();
-      point[3] = timelag;
+      point[3] = time_lag;
 
       out_of_range = false;
       for (std::size_t di = 0; di < config_.point_dim_size_; di++) {

perception/map_based_prediction/src/map_based_prediction_node.cpp

@@ -550,7 +550,7 @@ ObjectClassification::_label_type changeLabelForPrediction(
     label == ObjectClassification::MOTORCYCLE || label == ObjectClassification::BICYCLE) {
     // if object is within road lanelet and satisfies yaw constraints
     const bool within_road_lanelet = withinRoadLanelet(object, lanelet_map_ptr_, true);
-    const float high_speed_threshold = 25.0 / 18.0 * 5.0;  // High speed bycicle 25 km/h
+    const float high_speed_threshold = 25.0 / 18.0 * 5.0;  // High speed bicycle 25 km/h
     const bool high_speed_object =
       object.kinematics.twist_with_covariance.twist.linear.x > high_speed_threshold;