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perf(map_based_prediction): improve world to map transform calculation #8413

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merged 2 commits into from
Aug 14, 2024
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perf(map_based_prediction): improve world to map transform calculation #8413

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7abe6df
perf(map_based_prediction): improve world to map transform calculation
technolojin Aug 8, 2024
52cb9fc
perf(map_based_prediction): get transform only when it is necessary
technolojin Aug 13, 2024
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31 changes: 13 additions & 18 deletions perception/autoware_map_based_prediction/src/map_based_prediction_node.cpp
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
// Copyright 2021 Tier IV, Inc.

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CodeScene Delta Analysis / CodeScene Cloud Delta Analysis (main)

✅ Getting better: Lines of Code in a Single File 

The lines of code decreases from 1956 to 1950, improve code health by reducing it to 1000. The number of Lines of Code in a single file. More Lines of Code lowers the code health.

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CodeScene Delta Analysis / CodeScene Cloud Delta Analysis (main)

✅ Getting better: Overall Code Complexity 

The mean cyclomatic complexity decreases from 6.58 to 6.56, threshold = 4. This file has many conditional statements (e.g. if, for, while) across its implementation, leading to lower code health. Avoid adding more conditionals.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
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if (!lanelet_map_ptr_) {
return;
}

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CodeScene Delta Analysis / CodeScene Cloud Delta Analysis (main)

✅ No longer an issue: Complex Conditional 

MapBasedPredictionNode::objectsCallback no longer has a complex conditional. A complex conditional is an expression inside a branch (e.g. if, for, while) which consists of multiple, logical operators such as AND/OR. The more logical operators in an expression, the more severe the code smell.
auto world2map_transform = transform_listener_.getTransform(
"map", // target
in_objects->header.frame_id, // src
in_objects->header.stamp, rclcpp::Duration::from_seconds(0.1));
auto map2world_transform = transform_listener_.getTransform(
in_objects->header.frame_id, // target
"map", // src
in_objects->header.stamp, rclcpp::Duration::from_seconds(0.1));
auto debug_map2lidar_transform = transform_listener_.getTransform(
"base_link", // target
"map", // src
rclcpp::Time(), rclcpp::Duration::from_seconds(0.1));

if (!world2map_transform || !map2world_transform || !debug_map2lidar_transform) {
return;
}

// Remove old objects information in object history
const double objects_detected_time = rclcpp::Time(in_objects->header.stamp).seconds();
removeOldObjectsHistory(objects_detected_time, object_buffer_time_length_, road_users_history);
removeStaleTrafficLightInfo(in_objects);

auto invalidated_crosswalk_users = removeOldObjectsHistory(
objects_detected_time, object_buffer_time_length_, crosswalk_users_history_);
// delete matches that point to invalid object
for (auto it = known_matches_.begin(); it != known_matches_.end();) {
if (invalidated_crosswalk_users.count(it->second)) {
it = known_matches_.erase(it);
} else {
++it;
}
}

// result output
PredictedObjects output;
output.header = in_objects->header;
output.header.frame_id = "map";

// result debug
visualization_msgs::msg::MarkerArray debug_markers;

// get current crosswalk users for later prediction
std::unordered_map<std::string, TrackedObject> current_crosswalk_users;
for (const auto & object : in_objects->objects) {
const auto label_for_prediction =
changeLabelForPrediction(object.classification.front().label, object, lanelet_map_ptr_);
if (
label_for_prediction == ObjectClassification::PEDESTRIAN ||
label_for_prediction == ObjectClassification::BICYCLE) {
const std::string object_id = autoware::universe_utils::toHexString(object.object_id);
current_crosswalk_users.emplace(object_id, object);
}
}
std::unordered_set<std::string> predicted_crosswalk_users_ids;

// get world to map transform
geometry_msgs::msg::TransformStamped::ConstSharedPtr world2map_transform;

bool is_object_not_in_map_frame = in_objects->header.frame_id != "map";
if (is_object_not_in_map_frame) {
world2map_transform = transform_listener_.getTransform(
"map", // target
in_objects->header.frame_id, // src
in_objects->header.stamp, rclcpp::Duration::from_seconds(0.1));
if (!world2map_transform) return;
}

for (const auto & object : in_objects->objects) {
TrackedObject transformed_object = object;

// transform object frame if it's based on map frame
if (in_objects->header.frame_id != "map") {
if (is_object_not_in_map_frame) {

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CodeScene Delta Analysis / CodeScene Cloud Delta Analysis (main)

ℹ Getting worse: Bumpy Road Ahead 

MapBasedPredictionNode::objectsCallback increases from 10 to 11 logical blocks with deeply nested code, threshold is one single block per function. The Bumpy Road code smell is a function that contains multiple chunks of nested conditional logic. The deeper the nesting and the more bumps, the lower the code health.

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CodeScene Delta Analysis / CodeScene Cloud Delta Analysis (main)

✅ Getting better: Complex Method 

MapBasedPredictionNode::objectsCallback decreases in cyclomatic complexity from 46 to 45, threshold = 9. This function has many conditional statements (e.g. if, for, while), leading to lower code health. Avoid adding more conditionals and code to it without refactoring.
geometry_msgs::msg::PoseStamped pose_in_map;
geometry_msgs::msg::PoseStamped pose_orig;
pose_orig.pose = object.kinematics.pose_with_covariance.pose;
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