feat(lidar_centerpoint): optimize non-maximum suppression algorithm (#1689)

Refactor the `NonMaximumSuppression` class in the `lidar_centerpoint` module to optimize the non-maximum suppression algorithm. This includes removing the `isTargetLabel` function and modifying the `isTargetPairObject` function to handle pedestrian labels differently. Additionally, the `search_distance_2d_sq_` variable is now initialized in the `setParameters` function.

Author: technolojin

perception/lidar_centerpoint/include/lidar_centerpoint/postprocess/non_maximum_suppression.hpp

@@ -66,14 +66,14 @@ class NonMaximumSuppression
   std::vector<DetectedObject> apply(const std::vector<DetectedObject> &);
 
 private:
-  bool isTargetLabel(const std::uint8_t);
-
   bool isTargetPairObject(const DetectedObject &, const DetectedObject &);
 
   Eigen::MatrixXd generateIoUMatrix(const std::vector<DetectedObject> &);
 
   NMSParams params_{};
   std::vector<bool> target_class_mask_{};
+
+  double search_distance_2d_sq_{};
 };
 
 }  // namespace centerpoint

perception/lidar_centerpoint/lib/postprocess/non_maximum_suppression.cpp

@@ -19,38 +19,32 @@
 #include "object_recognition_utils/object_recognition_utils.hpp"
 namespace centerpoint
 {
+using Label = autoware_perception_msgs::msg::ObjectClassification;
 
 void NonMaximumSuppression::setParameters(const NMSParams & params)
 {
   assert(params.search_distance_2d_ >= 0.0);
   assert(params.iou_threshold_ >= 0.0 && params.iou_threshold_ <= 1.0);
 
   params_ = params;
+  search_distance_2d_sq_ = params.search_distance_2d_ * params.search_distance_2d_;
   target_class_mask_ = classNamesToBooleanMask(params.target_class_names_);
 }
 
-bool NonMaximumSuppression::isTargetLabel(const uint8_t label)
-{
-  if (label >= target_class_mask_.size()) {
-    return false;
-  }
-  return target_class_mask_.at(label);
-}
-
 bool NonMaximumSuppression::isTargetPairObject(
   const DetectedObject & object1, const DetectedObject & object2)
 {
   const auto label1 = object_recognition_utils::getHighestProbLabel(object1.classification);
   const auto label2 = object_recognition_utils::getHighestProbLabel(object2.classification);
 
-  if (isTargetLabel(label1) && isTargetLabel(label2)) {
-    return true;
+  // if labels are not the same, and one of them is pedestrian, do not suppress
+  if (label1 != label2 && (label1 == Label::PEDESTRIAN || label2 == Label::PEDESTRIAN)) {
+    return false;
   }
 
-  const auto search_sqr_dist_2d = params_.search_distance_2d_ * params_.search_distance_2d_;
   const auto sqr_dist_2d = autoware::universe_utils::calcSquaredDistance2d(
     object_recognition_utils::getPose(object1), object_recognition_utils::getPose(object2));
-  return sqr_dist_2d <= search_sqr_dist_2d;
+  return sqr_dist_2d <= search_distance_2d_sq_;
 }
 
 Eigen::MatrixXd NonMaximumSuppression::generateIoUMatrix(