Robots operating in the real world deal with constantly changing sensor inputs camera frames, LiDAR scans, and point clouds that rarely stay static. ROS dynamic reconfigure enables developers to tune parameters live without recompiling code, dramatically reducing development time and improving system performance.
This guide walks you through dynamic reconfigure in ROS, using examples with the Canny edge detector, LiDAR voxel grid filtering, and ROI cropping for images.
Why Dynamic Reconfigure Matters
When working with live robotics systems, static configuration files aren’t enough. For example:
- Changing thresholds for Canny edge detection to segment a road in a video feed.
- Adjusting leaf size in voxel grids to downsample LiDAR point clouds.
- Cropping a region of interest (ROI) without restarting your node.
Instead of editing launch files, rebuilding, and restarting, dynamic reconfigure lets you change parameters in real time. This approach:
- Saves hours of iteration.
- Makes parameter experimentation safe and reversible.
- Improves collaboration by letting teammates test changes live.
Setting Up a ROS2 Dynamic Reconfigure Node
Below is an example workflow using a camera feed to apply a Canny edge detector with adjustable thresholds.
1. Declare Parameters for ROS Dynamic Reconfigure
In your C++ node, declare and get parameters:
#include "rclcpp/rclcpp.hpp"
#include "sensor_msgs/msg/image.hpp"
#include <opencv2/opencv.hpp>
class CameraNode : public rclcpp::Node {
public:
CameraNode() : Node("camera_node") {
// Declare dynamic parameters
this->declare_parameter<int>("lower_threshold", 100);
this->declare_parameter<int>("upper_threshold", 200);
subscription_ = this->create_subscription<sensor_msgs::msg::Image>(
"/camera/image_raw", 10,
std::bind(&CameraNode::image_callback, this, std::placeholders::_1));
}
private:
void image_callback(const sensor_msgs::msg::Image::SharedPtr msg) {
int lower, upper;
this->get_parameter("lower_threshold", lower);
this->get_parameter("upper_threshold", upper);
cv::Mat gray, edges;
cv::Mat image = cv::Mat(msg->height, msg->width, CV_8UC3,
const_cast<unsigned char*>(msg->data.data()));
cv::cvtColor(image, gray, cv::COLOR_BGR2GRAY);
cv::Canny(gray, edges, lower, upper);
// Publish edges as new image...
}
rclcpp::Subscription<sensor_msgs::msg::Image>::SharedPtr subscription_;
};
int main(int argc, char **argv) {
rclcpp::init(argc, argv);
rclcpp::spin(std::make_shared<CameraNode>());
rclcpp::shutdown();
return 0;
}2. Launch RQT Dynamic Reconfigure
Run your node and open RQT:
ros2 run rqt_reconfigure rqt_reconfigureNow, adjust lower_threshold and upper_threshold sliders to instantly see changes in the edge-detected image no rebuild required.
Visualizing Parameter Effects
Here’s a sample chart showing how edge detection quality varies with different lower and upper thresholds for the Canny detector:
| Lower Threshold | Upper Threshold | Edges Detected (%) |
| 50 | 100 | 85% |
| 100 | 200 | 92% |
| 150 | 250 | 70% |
| 200 | 300 | 55% |
This highlights why live tuning matters you can rapidly find the sweet spot for your dataset.
Applying ROI Cropping for Image Streams
Dynamic reconfigure can also crop specific regions of a camera frame in real time. This is useful when you want to focus processing power on a lane marker or sidewalk.
Example: ROI Cropping
# Python example using cv_bridge and parameters
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import Image
from cv_bridge import CvBridge
import cv2
class ROICropNode(Node):
def __init__(self):
super().__init__('roi_crop_node')
self.declare_parameter('x_min', 0)
self.declare_parameter('x_max', 300)
self.sub = self.create_subscription(Image, '/camera/image_raw',
self.callback, 10)
self.bridge = CvBridge()
def callback(self, msg):
img = self.bridge.imgmsg_to_cv2(msg, 'bgr8')
x_min = self.get_parameter('x_min').value
x_max = self.get_parameter('x_max').value
cropped = img[:, x_min:x_max]
cv2.imshow('Cropped', cropped)
cv2.waitKey(1)
def main():
rclpy.init()
rclpy.spin(ROICropNode())
rclpy.shutdown()With RQT dynamic reconfigure, you can slide the cropping bounds live while watching the output, a huge time saver for tasks like ROI cropping ROS image.
LiDAR Example: Voxel Grid Filter Settings
For LiDAR point clouds, adjusting leaf size dynamically can reduce computational load without losing too much detail. Smaller leaf sizes preserve detail but increase processing time, while larger sizes downsample aggressively.
This principle is covered in detail in Point Cloud Library’s voxel grid filter guide.
Tips for Effective ROS Parameter Tuning
1. Start Wide, Narrow Down Gradually
Experiment with broad parameter ranges first. Quickly identify the approximate range before fine-tuning.
2. Use Visual Feedback
Whether in RViz for LiDAR or OpenCV for images, real-time visualization helps you understand parameter effects faster.
3. Document Best Values
Once you find optimal settings, add clear comments or YAML files documenting why these values were chosen. Future you or your teammates will thank you.
Benefits for Developers and Teams
- Faster Development: No need to stop, recompile, and restart.
- Collaboration: Teammates can test parameter changes without touching code.
- Reusable Code: Nodes become more versatile across datasets.
- Quality Output: Fine-tuning leads to cleaner edges, better point clouds, and more accurate detections.
These habits not only save your time but make your work more desirable by the team and more defensible when revisiting projects later.
FAQs
Q1: What versions of ROS support dynamic_reconfigure?
A1: ROS1 (e.g. Melodic, Noetic, etc.) supports dynamic_reconfigure. In ROS2, parameter services are built in differently; dynamic reconfigure as in ROS1 is not directly used but similar capabilities exist.
Q2: Does dynamic_reconfigure affect performance?
A2: Minimal. The overhead comes from monitoring for parameter changes and calling callback functions. If you’re making very frequent changes or doing heavy processing inside the callback, that can cause lag. Best practice: only do lightweight changes in callback (just update variables), heavy computation remains in main loops.
Q3: How to choose good threshold values for Canny edge detector?
A3: Use example images to test, start with default values like lower ≈ 50, upper ≈ 150, then tweak using GUI. Also understand lighting, noise, edge strength in your camera images. Tools like rqt_reconfigure help. Tutorial on Canny details: thresholds, gradient, hysteresis. OpenCV Documentation+1
Q4: Can I combine both edge detection and ROI cropping in one node?
A4: Yes. You can have a node with parameters for ROI (x_min, etc.) and parameters for edge detection thresholds. The image callback first crops, then converts to grayscale, then applies Canny. This helps reduce compute by focusing only on area of interest.
Q5: Are there safety or robustness concerns?
A5: Some: if parameters are set too extreme (e.g. zero upper threshold, or ROI outside image bounds), you might get errors or empty outputs. Always validate in code (bounds checking) and possibly set safe defaults. Also document why you chose certain parameter ranges so future maintainers understand.
Conclusion
ROS dynamic reconfigure is a powerful feature for tuning robotic software in real time. Whether you’re using Canny edge detector ROS, fine-tuning voxel grid filter settings, or experimenting with ROI cropping ROS image, dynamic reconfigure prevents unnecessary rebuilds and accelerates your workflow.
Adopting these practices leads to higher-quality results, smoother collaboration, and faster iteration—making your robotics projects efficient and future-proof. If you’re ready to deepen your expertise, explore Robotisim’s Robotics Learning Paths. Our hands-on, mentor-guided courses walk you through real-world robotics workflows—from simulation to hardware integration—so you can apply dynamic reconfigure and other advanced ROS techniques with confidence.