523 Distance measurement of not detected objects for radar data #525
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Actionable comments posted: 3
🧹 Outside diff range and nitpick comments (4)
code/perception/src/radar_node.py (4)
27-27: Avoid hardcoding parameters; make 'max_distance' configurablePassing a hardcoded value
10formax_distancereduces flexibility. Consider retrievingmax_distancefrom ROS parameters or making it a class attribute to allow dynamic configuration.Apply this diff to retrieve
max_distancefrom a ROS parameter:def callback(self, data): + max_distance = rospy.get_param("~max_distance", 10) - clustered_points = cluster_radar_data_from_pointcloud(data, 10) + clustered_points = cluster_radar_data_from_pointcloud(data, max_distance)
41-42: Update parameter name for consistencyThe parameter name
~image_distance_topicmay not accurately reflect the radar data being published. Consider renaming it to~radar_distance_topicfor clarity and consistency.Apply this diff:
self.dist_array_radar_publisher = rospy.Publisher( - rospy.get_param( - "~image_distance_topic", "/paf/hero/Radar/dist_array_unsegmented" - ), + rospy.get_param( + "~radar_distance_topic", "/paf/hero/Radar/dist_array_unsegmented" + ), String, queue_size=10, )Ensure that any configurations or launch files are updated accordingly.
66-85: Include docstrings for new functionsPlease add docstrings to the
pointcloud2_to_arrayandcluster_radar_data_from_pointcloudfunctions to explain their purpose, parameters, and return values. This improves code readability and maintainability.Apply this diff to add docstrings:
def pointcloud2_to_array(pointcloud_msg): + """Converts a PointCloud2 message into a NumPy array with x, y, z coordinates and distances. + + Args: + pointcloud_msg (PointCloud2): The PointCloud2 message to convert. + + Returns: + numpy.ndarray: An array with columns [x, y, z, distance]. + """ cloud_array = ros_numpy.point_cloud2.pointcloud2_to_array(pointcloud_msg) distances = np.sqrt( cloud_array["x"] ** 2 + cloud_array["y"] ** 2 + cloud_array["z"] ** 2 ) return np.column_stack( (cloud_array["x"], cloud_array["y"], cloud_array["z"], distances) ) def cluster_radar_data_from_pointcloud( pointcloud_msg, max_distance, eps=1.0, min_samples=2 ): + """Clusters radar data points from a PointCloud2 message using DBSCAN. + + Args: + pointcloud_msg (PointCloud2): The PointCloud2 message containing radar data. + max_distance (float): Maximum distance to consider for data points. + eps (float, optional): The maximum distance between two samples for them to be considered as in the same neighborhood. + min_samples (int, optional): The number of samples in a neighborhood for a point to be considered as a core point. + + Returns: + dict: A dictionary where keys are cluster labels and values are the count of points in each cluster. + """ data = pointcloud2_to_array(pointcloud_msg) filtered_data = data[data[:, 3] < max_distance] # Rest of the function...
77-78: Log a warning when no data points are found after filteringIf
filtered_datais empty, it might be helpful to log a warning to inform that no data points met the filtering criteria.Apply this diff:
if len(filtered_data) == 0: + rospy.logwarn("No data points found after filtering in cluster_radar_data_from_pointcloud.") return {}
| from std_msgs.msg import String | ||
| from sensor_msgs.msg import PointCloud2 | ||
| from std_msgs.msg import Float32 | ||
| from sklearn.cluster import DBSCAN | ||
| import json |
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Ensure 'scikit-learn' is included in project dependencies
The DBSCAN class from sklearn.cluster is being used. Please ensure that scikit-learn is added to your project's dependencies in requirements.txt or setup.py so that it is installed during setup.
code/perception/src/radar_node.py
Outdated
| """Process radar Point2Cloud data and publish clustered data points. | ||
|
|
||
| Extracts velocity information from radar data | ||
| and publishes the minimum velocity as a | ||
| Float32 message | ||
| Extracts information from radar data | ||
| and publishes the clustered radar data | ||
| points as a String message | ||
|
|
||
| Args: | ||
| data: Point2Cloud message containing radar data with velocity field | ||
| data: Point2Cloud message containing radar data |
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🛠️ Refactor suggestion
Fix typos and improve docstring formatting
- Replace
Point2CloudwithPointCloud2to correct the typo. - Adjust the docstring to follow PEP 257 conventions by adding a blank line after the summary and consolidating the description.
Apply this diff to correct the docstring:
def callback(self, data):
- """Process radar Point2Cloud data and publish clustered data points.
-
- Extracts information from radar data
- and publishes the clustered radar data
- points as a String message
+ """Process radar PointCloud2 data and publish clustered data points.
+
+ Extracts information from radar data and publishes the clustered radar data
+ points as a `String` message.
Args:
- data: Point2Cloud message containing radar data
+ data: PointCloud2 message containing radar data
"""📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| """Process radar Point2Cloud data and publish clustered data points. | |
| Extracts velocity information from radar data | |
| and publishes the minimum velocity as a | |
| Float32 message | |
| Extracts information from radar data | |
| and publishes the clustered radar data | |
| points as a String message | |
| Args: | |
| data: Point2Cloud message containing radar data with velocity field | |
| data: Point2Cloud message containing radar data | |
| """Process radar PointCloud2 data and publish clustered data points. | |
| Extracts information from radar data and publishes the clustered radar data | |
| points as a `String` message. | |
| Args: | |
| data: PointCloud2 message containing radar data |
code/perception/src/radar_node.py
Outdated
| def pointcloud2_to_array(pointcloud_msg): | ||
| cloud_array = ros_numpy.point_cloud2.pointcloud2_to_array(pointcloud_msg) | ||
| distances = np.sqrt( | ||
| cloud_array["x"] ** 2 + cloud_array["y"] ** 2 + cloud_array["z"] ** 2 | ||
| ) | ||
| return np.column_stack( | ||
| (cloud_array["x"], cloud_array["y"], cloud_array["z"], distances) | ||
| ) |
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🛠️ Refactor suggestion
Add exception handling for point cloud conversion
Converting the PointCloud2 message to an array may raise exceptions if the message is malformed. Add exception handling to prevent the node from crashing due to unexpected input.
Apply this diff to add exception handling:
def pointcloud2_to_array(pointcloud_msg):
+ try:
cloud_array = ros_numpy.point_cloud2.pointcloud2_to_array(pointcloud_msg)
+ except Exception as e:
+ rospy.logerr(f"Failed to convert PointCloud2 to array: {e}")
+ return np.array([])
distances = np.sqrt(
cloud_array["x"] ** 2 + cloud_array["y"] ** 2 + cloud_array["z"] ** 2
)
return np.column_stack(
(cloud_array["x"], cloud_array["y"], cloud_array["z"], distances)
)Committable suggestion skipped: line range outside the PR's diff.
New functions have been annotated with comments.
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Actionable comments posted: 3
🧹 Outside diff range and nitpick comments (2)
code/perception/src/radar_node.py (2)
58-69: Translate German documentation to EnglishFor consistency and maintainability, documentation should be in English.
Apply this diff:
- Konvertiert eine ROS-PointCloud2-Nachricht in ein NumPy-Array und berechnet die euklidischen Entfernungen - jedes Punkts vom Ursprung. + Converts a ROS PointCloud2 message to a NumPy array and calculates the Euclidean distances + of each point from the origin. - Parameter: + Args: - pointcloud_msg: sensor_msgs/PointCloud2 - Die ROS-PointCloud2-Nachricht, die die 3D-Punkte enthält. + The ROS PointCloud2 message containing 3D points. - Rückgabewert: + Returns: - np.ndarray - Ein 2D-Array, in dem jede Zeile einem Punkt in der Punktwolke entspricht: - [x, y, z, distance], wobei "distance" die Entfernung vom Ursprung ist. + A 2D array where each row corresponds to a point in the point cloud: + [x, y, z, distance], where "distance" is the distance from origin.
84-106: Translate German documentation to EnglishFor consistency and maintainability, documentation should be in English.
Apply this diff:
- Filtert und gruppiert Punkte aus einer ROS-PointCloud2-Nachricht basierend auf DBSCAN-Clustering. + Filters and groups points from a ROS PointCloud2 message based on DBSCAN clustering. - Parameter: + Args: - pointcloud_msg: sensor_msgs/PointCloud2 - Die ROS-PointCloud2-Nachricht mit den 3D-Punkten. + The ROS PointCloud2 message containing 3D points. - max_distance: float - Maximale Entfernung, um Punkte zu berücksichtigen. Punkte außerhalb dieser Entfernung werden verworfen. + Maximum distance to consider points. Points beyond this distance are discarded. - eps: float, optional (default: 1.0) - Der maximale Abstand zwischen zwei Punkten, damit diese im selben Cluster sein können. + The maximum distance between two points to be considered in the same cluster. - min_samples: int, optional (default: 2) - Die minimale Anzahl von Punkten, die erforderlich sind, um einen Cluster zu bilden. + The minimum number of points required to form a cluster. - Rückgabewert: + Returns: - dict - Ein Dictionary, in dem die Schlüssel die Cluster-Labels (int) sind und die Werte die Anzahl der Punkte - in jedem Cluster. Wenn keine Punkte vorhanden sind, wird ein leeres Dictionary zurückgegeben. + A dictionary where keys are cluster labels (int) and values are the number of points + in each cluster. Returns an empty dictionary if no points are present. - Funktionalität: + Functionality: - - Die Funktion konvertiert die Punktwolke in ein Array mit Punktkoordinaten und berechneten Entfernungen. - - Punkte außerhalb eines spezifizierten Entfernungsbereichs und räumlichen Filtergrenzen werden verworfen. - - Anschließend wird DBSCAN-Clustering auf die 2D-Koordinaten (x, y) der Punkte angewendet. - - Die Cluster-Labels und ihre Häufigkeiten werden als Dictionary zurückgegeben. + - Converts the point cloud to an array with point coordinates and calculated distances. + - Discards points outside specified distance range and spatial filter boundaries. + - Applies DBSCAN clustering on the 2D coordinates (x, y) of the points. + - Returns cluster labels and their frequencies as a dictionary.
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code/perception/src/radar_node.py(4 hunks)
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code/perception/src/radar_node.py (1)
126-127: Verify DBSCAN clustering parameters
The default parameters (eps=1.0, min_samples=2) might need adjustment based on your radar's characteristics and the expected object sizes/distances.
Run this script to analyze the clustering results with different parameters:
✅ Verification successful
Let me gather more context about the parameter values and their configuration.
Let me check the function's usage and any test cases that might validate these parameters.
Let me check the radar node implementation to understand how these parameters are being used in the broader context.
DBSCAN parameters are appropriately configured for radar clustering
The current DBSCAN parameters (eps=1.0, min_samples=2) are well-suited for radar point clustering because:
- The implementation includes proper spatial filtering (y: [-1,1], z: [-0.7,1.3]) before clustering
- The clustering is performed on 2D coordinates (x,y) which is appropriate for radar-based object detection
- The eps=1.0 (meter) is a reasonable distance threshold for grouping radar points from the same object
- The min_samples=2 is suitable for radar data as it allows detection of small objects while filtering noise
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Analyze clustering results with different parameters
# Expected: Find optimal eps and min_samples values
# Search for any existing parameter tuning tests or documentation
rg -g '!*.pyc' -g '!*.git*' -A 5 'DBSCAN.*eps.*min_samples'
# Search for radar-specific configuration files
fd -e yaml -e launch radar
Length of output: 536
Script:
#!/bin/bash
# Search for parameter definitions and configurations
rg -g '!*.pyc' -g '!*.git*' -A 5 'eps.*=|min_samples.*=' code/perception/
Length of output: 914
Script:
#!/bin/bash
# Search for function calls and test cases
rg -g '!*.pyc' -g '!*.git*' "filter_and_group_points" code/perception/
Length of output: 70
Script:
#!/bin/bash
# Get the full radar node implementation
cat code/perception/src/radar_node.py
Length of output: 4862
| def cluster_radar_data_from_pointcloud( | ||
| pointcloud_msg, max_distance, eps=1.0, min_samples=2 | ||
| ): |
There was a problem hiding this comment.
Add input parameter validation
The function should validate input parameters to prevent invalid configurations.
Apply this diff:
def cluster_radar_data_from_pointcloud(
pointcloud_msg, max_distance, eps=1.0, min_samples=2
):
+ if max_distance <= 0:
+ raise ValueError("max_distance must be positive")
+ if eps <= 0:
+ raise ValueError("eps must be positive")
+ if min_samples < 2:
+ raise ValueError("min_samples must be at least 2")
+ if not isinstance(pointcloud_msg, PointCloud2):
+ raise TypeError("pointcloud_msg must be a PointCloud2 message")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def cluster_radar_data_from_pointcloud( | |
| pointcloud_msg, max_distance, eps=1.0, min_samples=2 | |
| ): | |
| def cluster_radar_data_from_pointcloud( | |
| pointcloud_msg, max_distance, eps=1.0, min_samples=2 | |
| ): | |
| if max_distance <= 0: | |
| raise ValueError("max_distance must be positive") | |
| if eps <= 0: | |
| raise ValueError("eps must be positive") | |
| if min_samples < 2: | |
| raise ValueError("min_samples must be at least 2") | |
| if not isinstance(pointcloud_msg, PointCloud2): | |
| raise TypeError("pointcloud_msg must be a PointCloud2 message") |
| clustered_points = cluster_radar_data_from_pointcloud(data, 10) | ||
| clustered_points_json = json.dumps(clustered_points) | ||
| self.dist_array_radar_publisher.publish(clustered_points_json) |
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Add error handling for JSON serialization
The callback should handle potential exceptions during clustering and JSON serialization to prevent node crashes.
Apply this diff:
- clustered_points = cluster_radar_data_from_pointcloud(data, 10)
- clustered_points_json = json.dumps(clustered_points)
- self.dist_array_radar_publisher.publish(clustered_points_json)
+ try:
+ clustered_points = cluster_radar_data_from_pointcloud(data, 10)
+ clustered_points_json = json.dumps(clustered_points)
+ self.dist_array_radar_publisher.publish(clustered_points_json)
+ except Exception as e:
+ rospy.logerr(f"Failed to process radar data: {e}")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| clustered_points = cluster_radar_data_from_pointcloud(data, 10) | |
| clustered_points_json = json.dumps(clustered_points) | |
| self.dist_array_radar_publisher.publish(clustered_points_json) | |
| try: | |
| clustered_points = cluster_radar_data_from_pointcloud(data, 10) | |
| clustered_points_json = json.dumps(clustered_points) | |
| self.dist_array_radar_publisher.publish(clustered_points_json) | |
| except Exception as e: | |
| rospy.logerr(f"Failed to process radar data: {e}") |
| filtered_data = filtered_data[ | ||
| (filtered_data[:, 1] >= -1) | ||
| & (filtered_data[:, 1] <= 1) | ||
| & (filtered_data[:, 2] <= 1.3) | ||
| & (filtered_data[:, 2] >= -0.7) | ||
| ] |
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🛠️ Refactor suggestion
Make filter boundaries configurable via ROS parameters
Hard-coded filter values should be configurable to allow for easy adjustment without code changes.
Apply this diff:
+ # Get filter boundaries from ROS parameters with defaults
+ y_min = rospy.get_param("~filter/y_min", -1.0)
+ y_max = rospy.get_param("~filter/y_max", 1.0)
+ z_min = rospy.get_param("~filter/z_min", -0.7)
+ z_max = rospy.get_param("~filter/z_max", 1.3)
+
filtered_data = filtered_data[
- (filtered_data[:, 1] >= -1)
- & (filtered_data[:, 1] <= 1)
- & (filtered_data[:, 2] <= 1.3)
- & (filtered_data[:, 2] >= -0.7)
+ (filtered_data[:, 1] >= y_min)
+ & (filtered_data[:, 1] <= y_max)
+ & (filtered_data[:, 2] <= z_max)
+ & (filtered_data[:, 2] >= z_min)
]📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| filtered_data = filtered_data[ | |
| (filtered_data[:, 1] >= -1) | |
| & (filtered_data[:, 1] <= 1) | |
| & (filtered_data[:, 2] <= 1.3) | |
| & (filtered_data[:, 2] >= -0.7) | |
| ] | |
| # Get filter boundaries from ROS parameters with defaults | |
| y_min = rospy.get_param("~filter/y_min", -1.0) | |
| y_max = rospy.get_param("~filter/y_max", 1.0) | |
| z_min = rospy.get_param("~filter/z_min", -0.7) | |
| z_max = rospy.get_param("~filter/z_max", 1.3) | |
| filtered_data = filtered_data[ | |
| (filtered_data[:, 1] >= y_min) | |
| & (filtered_data[:, 1] <= y_max) | |
| & (filtered_data[:, 2] <= z_max) | |
| & (filtered_data[:, 2] >= z_min) | |
| ] |
Whitespace elimination.
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Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (2)
code/perception/src/radar_node.py (2)
27-27: Make max_distance configurable via ROS parameterThe max_distance value is hardcoded to 10. Consider making it configurable via ROS parameters for better flexibility.
- clustered_points = cluster_radar_data_from_pointcloud(data, 10) + max_distance = rospy.get_param('~clustering/max_distance', 10.0) + clustered_points = cluster_radar_data_from_pointcloud(data, max_distance)
124-126: Consider enhancing clustering capabilitiesThe current implementation performs clustering only on 2D coordinates (x, y). Consider these potential improvements:
- Use 3D coordinates for clustering to capture vertical patterns
- Incorporate velocity data (if available in radar points) for better object separation
Example enhancement:
- # Extrahiere die 2D-Koordinaten (x, y) für das Clustering - coordinates = filtered_data[:, :2] + # Use 3D coordinates for clustering + coordinates = filtered_data[:, :3]
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code/perception/src/radar_node.py(4 hunks)
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code/perception/src/radar_node.py (1)
40-43: LGTM: Publisher configuration is well-structured
The publisher configuration follows ROS conventions and uses appropriate parameters for JSON message publishing.
code/perception/src/radar_node.py
Outdated
| Konvertiert eine ROS-PointCloud2-Nachricht in ein NumPy-Array und berechnet die euklidischen Entfernungen | ||
| jedes Punkts vom Ursprung. | ||
|
|
||
| Parameter: | ||
| - pointcloud_msg: sensor_msgs/PointCloud2 | ||
| Die ROS-PointCloud2-Nachricht, die die 3D-Punkte enthält. | ||
|
|
||
| Rückgabewert: | ||
| - np.ndarray | ||
| Ein 2D-Array, in dem jede Zeile einem Punkt in der Punktwolke entspricht: | ||
| [x, y, z, distance], wobei "distance" die Entfernung vom Ursprung ist. | ||
| """ |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Translate documentation to English for consistency
The docstrings are written in German while the rest of the codebase appears to be in English. Consider translating them for better maintainability and consistency.
Example translation for pointcloud2_to_array:
- Konvertiert eine ROS-PointCloud2-Nachricht in ein NumPy-Array und berechnet die euklidischen Entfernungen
- jedes Punkts vom Ursprung.
+ Converts a ROS PointCloud2 message to a NumPy array and calculates the Euclidean distances
+ of each point from the origin.
- Parameter:
+ Parameters:
- pointcloud_msg: sensor_msgs/PointCloud2
- Die ROS-PointCloud2-Nachricht, die die 3D-Punkte enthält.
+ The ROS PointCloud2 message containing 3D points.
- Rückgabewert:
+ Returns:
- np.ndarray
- Ein 2D-Array, in dem jede Zeile einem Punkt in der Punktwolke entspricht:
- [x, y, z, distance], wobei "distance" die Entfernung vom Ursprung ist.
+ A 2D array where each row corresponds to a point in the point cloud:
+ [x, y, z, distance], where "distance" is the distance from origin.Also applies to: 84-106
maximal line length of 88 characters.
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🧹 Outside diff range and nitpick comments (4)
code/perception/src/radar_node.py (4)
27-27: Make clustering parameters configurable via ROS parametersThe max_distance parameter is hardcoded to 10. Consider making this configurable via ROS parameters for flexibility.
- clustered_points = cluster_radar_data_from_pointcloud(data, 10) + max_distance = rospy.get_param("~clustering/max_distance", 10.0) + eps = rospy.get_param("~clustering/eps", 1.0) + min_samples = rospy.get_param("~clustering/min_samples", 2) + clustered_points = cluster_radar_data_from_pointcloud( + data, max_distance, eps, min_samples + )
139-148: Simplify cluster counting logicThe current implementation of counting cluster points can be simplified using numpy:
- # Zählen der Punktanzahl in jedem Cluster - clustered_points = { - label: list(labels).count(label) - for label in set(labels) - } - - # Konvertiere die Labels in Integer und gebe das Dictionary zurück - clustered_points = { - int(label): count - for label, count in clustered_points.items() - } + # Count points in each cluster using numpy + unique_labels, counts = np.unique(labels, return_counts=True) + clustered_points = dict(zip(unique_labels.astype(int), counts))This approach is more efficient as it:
- Uses numpy's optimized counting functions
- Avoids multiple dictionary comprehensions
- Performs the integer conversion in a single step
134-136: Consider adding cluster quality metricsThe clustering results could benefit from additional quality metrics to help evaluate the effectiveness of the clustering:
coordinates = filtered_data[:, :2] clustering = DBSCAN(eps=eps, min_samples=min_samples).fit(coordinates) labels = clustering.labels_ + + # Calculate clustering quality metrics + n_clusters = len(set(labels)) - (1 if -1 in labels else 0) + n_noise = list(labels).count(-1) + rospy.logdebug(f"Found {n_clusters} clusters and {n_noise} noise points")
81-83: Add unit tests for clustering functionalityGiven the complexity of the clustering logic and its importance for object detection, comprehensive unit tests should be added to verify:
- Clustering behavior with various point distributions
- Edge cases (empty point clouds, single points, etc.)
- Filter boundary conditions
- Output format consistency
Would you like me to help generate unit tests for these scenarios?
| cloud_array = ros_numpy.point_cloud2.pointcloud2_to_array(pointcloud_msg) | ||
| distances = np.sqrt( | ||
| cloud_array["x"] ** 2 + cloud_array["y"] ** 2 + cloud_array["z"] ** 2 | ||
| ) |
There was a problem hiding this comment.
Add error handling for numpy operations
The numpy operations could raise exceptions with invalid input data. Consider adding error handling:
- cloud_array = ros_numpy.point_cloud2.pointcloud2_to_array(pointcloud_msg)
- distances = np.sqrt(
- cloud_array["x"] ** 2 + cloud_array["y"] ** 2 + cloud_array["z"] ** 2
- )
+ try:
+ cloud_array = ros_numpy.point_cloud2.pointcloud2_to_array(pointcloud_msg)
+ distances = np.sqrt(
+ cloud_array["x"] ** 2 + cloud_array["y"] ** 2 + cloud_array["z"] ** 2
+ )
+ except (ValueError, KeyError) as e:
+ rospy.logerr(f"Error processing point cloud data: {e}")
+ return np.array([])📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| cloud_array = ros_numpy.point_cloud2.pointcloud2_to_array(pointcloud_msg) | |
| distances = np.sqrt( | |
| cloud_array["x"] ** 2 + cloud_array["y"] ** 2 + cloud_array["z"] ** 2 | |
| ) | |
| try: | |
| cloud_array = ros_numpy.point_cloud2.pointcloud2_to_array(pointcloud_msg) | |
| distances = np.sqrt( | |
| cloud_array["x"] ** 2 + cloud_array["y"] ** 2 + cloud_array["z"] ** 2 | |
| ) | |
| except (ValueError, KeyError) as e: | |
| rospy.logerr(f"Error processing point cloud data: {e}") | |
| return np.array([]) |
Translated comments to englisch and reformated code.
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🧹 Outside diff range and nitpick comments (3)
code/perception/src/radar_node.py (3)
64-66: Consider optimizing memory usageThe
column_stackoperation creates a new array in memory. For large point clouds, consider using pre-allocated arrays or in-place operations.- return np.column_stack( - (cloud_array["x"], cloud_array["y"], cloud_array["z"], distances) - ) + result = np.empty((len(cloud_array), 4), dtype=np.float32) + result[:, 0] = cloud_array["x"] + result[:, 1] = cloud_array["y"] + result[:, 2] = cloud_array["z"] + result[:, 3] = distances + return result
104-108: Enhance clustering output with additional metricsConsider including more useful metrics for each cluster, such as centroid and bounding box, to provide more context for downstream processing.
- clustered_points = {label: list(labels).count(label) for label in set(labels)} - clustered_points = {int(label): count for label, count in clustered_points.items()} + clustered_points = {} + for label in set(labels): + if label == -1: # Skip noise points + continue + mask = labels == label + points = coordinates[mask] + clustered_points[int(label)] = { + 'count': np.sum(mask), + 'centroid': points.mean(axis=0).tolist(), + 'bbox': { + 'min': points.min(axis=0).tolist(), + 'max': points.max(axis=0).tolist() + } + }
104-105: Consider optimizing DBSCAN for real-time performanceFor real-time radar processing, consider using more efficient clustering methods or optimizing DBSCAN parameters:
- Use
algorithm='ball_tree'for better performance with 2D data- Set
n_jobs=-1to utilize multiple CPU cores- clustering = DBSCAN(eps=eps, min_samples=min_samples).fit(coordinates) + clustering = DBSCAN( + eps=eps, + min_samples=min_samples, + algorithm='ball_tree', + n_jobs=-1 + ).fit(coordinates)
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code/perception/src/radar_node.py (1)
31-37: LGTM! Publisher configuration looks good.
The publisher configuration follows ROS conventions and properly uses parameter overrides for topic names.
| coordinates = ros_numpy.point_cloud2.pointcloud2_to_array(data) | ||
| msg = np.min(coordinates["Velocity"]) | ||
| self.dist_array_radar_publisher.publish(msg) | ||
| clustered_points = cluster_radar_data_from_pointcloud(data, 10) |
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🛠️ Refactor suggestion
Make clustering parameters configurable via ROS parameters
The max_distance parameter is hardcoded to 10. Consider making this configurable via ROS parameters for better flexibility.
- clustered_points = cluster_radar_data_from_pointcloud(data, 10)
+ max_distance = rospy.get_param('~clustering/max_distance', 10.0)
+ eps = rospy.get_param('~clustering/eps', 1.0)
+ min_samples = rospy.get_param('~clustering/min_samples', 2)
+ clustered_points = cluster_radar_data_from_pointcloud(
+ data, max_distance, eps, min_samples
+ )Committable suggestion skipped: line range outside the PR's diff.
asamluka
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523-general-distance-measurement-of-not-detected-objects-for-radar-data
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Description
Added clustering for radar data points
Fixes # (523)
Type of change
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Does this PR introduce a breaking change?
No
Most important changes
radar_node
Checklist:
Summary by CodeRabbit
New Features
Bug Fixes