isaac-ros-vslam-perception
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# python/vslam_pipeline.py
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import Image, CameraInfo
from nav_msgs.msg import Odometry
from geometry_msgs.msg import PoseStamped
from cv_bridge import CvBridge
import numpy as np
import cv2
from isaac_ros_seesaw import SeesawVSLAM
class IsaacVSLAMNode(Node):
def __init__(self):
super().__init__('isaac_vslam_node')
# ROS 2 interfaces
self.bridge = CvBridge()
self.odom_publisher = self.create_publisher(Odometry, '/visual_odom', 10)
self.pose_publisher = self.create_publisher(PoseStamped, '/vslam_pose', 10)
# Subscribe to camera topics
self.image_sub = self.create_subscription(
Image,
'/camera/rgb/image_raw',
self.image_callback,
10
)
self.camera_info_sub = self.create_subscription(
CameraInfo,
'/camera/rgb/camera_info',
self.camera_info_callback,
10
)
# Initialize Isaac VSLAM
self.vslam = SeesawVSLAM(
max_features=2000,
min_distance=15,
matching_threshold=0.7
)
# State variables
self.camera_matrix = None
self.distortion_coeffs = None
self.previous_frame = None
self.current_pose = np.eye(4)
self.get_logger().info("Isaac VSLAM Node initialized")
def camera_info_callback(self, msg):
"""Process camera calibration information"""
if self.camera_matrix is None:
self.camera_matrix = np.array(msg.k).reshape(3, 3)
self.distortion_coeffs = np.array(msg.d)
self.get_logger().info(f"Camera calibration loaded: {self.camera_matrix.shape}")
def image_callback(self, msg):
"""Process incoming camera images for VSLAM"""
try:
# Convert ROS Image to OpenCV
cv_image = self.bridge.imgmsg_to_cv2(msg, desired_encoding='passthrough')
# Undistort image if camera parameters available
if self.camera_matrix is not None and self.distortion_coeffs is not None:
cv_image = cv2.undistort(
cv_image,
self.camera_matrix,
self.distortion_coeffs,
None,
self.camera_matrix
)
# Run VSLAM
success, pose_increment = self.vslam.process_frame(cv_image)
if success:
# Update current pose
self.current_pose = self.current_pose @ pose_increment
# Publish odometry
self.publish_odometry(msg.header.stamp)
# Publish pose
self.publish_pose(msg.header.stamp)
except Exception as e:
self.get_logger().error(f"Error processing image: {e}")
def publish_odometry(self, timestamp):
"""Publish visual odometry"""
odom_msg = Odometry()
odom_msg.header.stamp = timestamp
odom_msg.header.frame_id = "map"
odom_msg.child_frame_id = "base_link"
# Extract position and orientation from pose matrix
position = self.current_pose[:3, 3]
orientation = self.rotation_matrix_to_quaternion(self.current_pose[:3, :3])
odom_msg.pose.pose.position.x = float(position[0])
odom_msg.pose.pose.position.y = float(position[1])
odom_msg.pose.pose.position.z = float(position[2])
odom_msg.pose.pose.orientation.x = float(orientation[0])
odom_msg.pose.pose.orientation.y = float(orientation[1])
odom_msg.pose.pose.orientation.z = float(orientation[2])
odom_msg.pose.pose.orientation.w = float(orientation[3])
self.odom_publisher.publish(odom_msg)
def publish_pose(self, timestamp):
"""Publish pose estimate"""
pose_msg = PoseStamped()
pose_msg.header.stamp = timestamp
pose_msg.header.frame_id = "map"
position = self.current_pose[:3, 3]
orientation = self.rotation_matrix_to_quaternion(self.current_pose[:3, :3])
pose_msg.pose.position.x = float(position[0])
pose_msg.pose.position.y = float(position[1])
pose_msg.pose.position.z = float(position[2])
pose_msg.pose.orientation.x = float(orientation[0])
pose_msg.pose.orientation.y = float(orientation[1])
pose_msg.pose.orientation.z = float(orientation[2])
pose_msg.pose.orientation.w = float(orientation[3])
self.pose_publisher.publish(pose_msg)
def rotation_matrix_to_quaternion(self, rotation_matrix):
"""Convert rotation matrix to quaternion"""
# Method to convert rotation matrix to quaternion
trace = np.trace(rotation_matrix)
if trace > 0:
s = np.sqrt(trace + 1.0) * 2 # s = 4 * qw
qw = 0.25 * s
qx = (rotation_matrix[2, 1] - rotation_matrix[1, 2]) / s
qy = (rotation_matrix[0, 2] - rotation_matrix[2, 0]) / s
qz = (rotation_matrix[1, 0] - rotation_matrix[0, 1]) / s
else:
if rotation_matrix[0, 0] > rotation_matrix[1, 1] and rotation_matrix[0, 0] > rotation_matrix[2, 2]:
s = np.sqrt(1.0 + rotation_matrix[0, 0] - rotation_matrix[1, 1] - rotation_matrix[2, 2]) * 2
qw = (rotation_matrix[2, 1] - rotation_matrix[1, 2]) / s
qx = 0.25 * s
qy = (rotation_matrix[0, 1] + rotation_matrix[1, 0]) / s
qz = (rotation_matrix[0, 2] + rotation_matrix[2, 0]) / s
elif rotation_matrix[1, 1] > rotation_matrix[2, 2]:
s = np.sqrt(1.0 + rotation_matrix[1, 1] - rotation_matrix[0, 0] - rotation_matrix[2, 2]) * 2
qw = (rotation_matrix[0, 2] - rotation_matrix[2, 0]) / s
qx = (rotation_matrix[0, 1] + rotation_matrix[1, 0]) / s
qy = 0.25 * s
qz = (rotation_matrix[1, 2] + rotation_matrix[2, 1]) / s
else:
s = np.sqrt(1.0 + rotation_matrix[2, 2] - rotation_matrix[0, 0] - rotation_matrix[1, 1]) * 2
qw = (rotation_matrix[1, 0] - rotation_matrix[0, 1]) / s
qx = (rotation_matrix[0, 2] + rotation_matrix[2, 0]) / s
qy = (rotation_matrix[1, 2] + rotation_matrix[2, 1]) / s
qz = 0.25 * s
return [qx, qy, qz, qw]
def main(args=None):
rclpy.init(args=args)
vslam_node = IsaacVSLAMNode()
try:
rclpy.spin(vslam_node)
except KeyboardInterrupt:
pass
finally:
vslam_node.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()
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# python/accelerated_feature_detection.py
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import Image
from std_msgs.msg import Header
from cv_bridge import CvBridge
import numpy as np
import cv2
from isaac_ros.common import IsaacROSBase
import time
class AcceleratedFeatureDetectionNode(Node):
def __init__(self):
super().__init__('accelerated_feature_detection_node')
self.bridge = CvBridge()
# Publishers and subscribers
self.image_sub = self.create_subscription(
Image,
'/camera/rgb/image_raw',
self.image_callback,
10
)
# Feature detector parameters
self.max_features = 2000
self.min_distance = 15
self.quality_level = 0.01
self.block_size = 3
# Initialize CUDA-based feature detector if available
self.use_cuda = self.check_cuda_support()
if self.use_cuda:
self.get_logger().info("CUDA-accelerated feature detection enabled")
else:
self.get_logger().info("Falling back to CPU-based feature detection")
self.get_logger().info("Accelerated feature detection node initialized")
def check_cuda_support(self):
"""Check if CUDA is available for accelerated processing"""
try:
import pycuda.driver as cuda
import pycuda.autoinit
cuda.init()
return True
except ImportError:
self.get_logger().warn("PyCUDA not available, using CPU processing")
return False
except Exception as e:
self.get_logger().warn(f"CUDA not available: {e}")
return False
def image_callback(self, msg):
"""Process image and detect features"""
try:
start_time = time.time()
# Convert ROS Image to OpenCV
cv_image = self.bridge.imgmsg_to_cv2(msg, desired_encoding='passthrough')
# Convert to grayscale if needed
if len(cv_image.shape) == 3:
gray = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY)
else:
gray = cv_image
# Detect features
if self.use_cuda:
keypoints = self.cuda_feature_detection(gray)
else:
keypoints = self.cpu_feature_detection(gray)
# Measure processing time
processing_time = time.time() - start_time
# Log performance
self.get_logger().debug(f"Feature detection: {len(keypoints)} features in {processing_time:.3f}s")
except Exception as e:
self.get_logger().error(f"Error in feature detection: {e}")
def cuda_feature_detection(self, gray_image):
"""Perform feature detection using CUDA acceleration"""
# In practice, this would use NVIDIA's optimized CUDA kernels
# For now, we'll use a simplified approach that demonstrates the concept
# Detect Shi-Tomasi corners (good features to track)
corners = cv2.goodFeaturesToTrack(
gray_image,
maxCorners=self.max_features,
qualityLevel=self.quality_level,
minDistance=self.min_distance,
blockSize=self.block_size
)
if corners is not None:
keypoints = [cv2.KeyPoint(x=float(point[0][0]), y=float(point[0][1]), size=1) for point in corners]
else:
keypoints = []
return keypoints
def cpu_feature_detection(self, gray_image):
"""Perform feature detection on CPU"""
# Detect Shi-Tomasi corners
corners = cv2.goodFeaturesToTrack(
gray_image,
maxCorners=self.max_features,
qualityLevel=self.quality_level,
minDistance=self.min_distance,
blockSize=self.block_size
)
if corners is not None:
keypoints = [cv2.KeyPoint(x=float(point[0][0]), y=float(point[0][1]), size=1) for point in corners]
else:
keypoints = []
return keypoints
def extract_descriptors(self, image, keypoints):
"""Extract feature descriptors using SIFT or ORB"""
# Use ORB as it's faster and works well with GPU acceleration
orb = cv2.ORB_create(nfeatures=self.max_features)
keypoints, descriptors = orb.detectAndCompute(image, None)
return keypoints, descriptors
def main(args=None):
rclpy.init(args=args)
feature_node = AcceleratedFeatureDetectionNode()
try:
rclpy.spin(feature_node)
except KeyboardInterrupt:
pass
finally:
feature_node.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()
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# python/stereo_perception_pipeline.py
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import Image, CameraInfo
from stereo_msgs.msg import DisparityImage
from geometry_msgs.msg import PointStamped
import numpy as np
import cv2
from cv_bridge import CvBridge
from scipy.spatial.transform import Rotation as R
class IsaacStereoPerceptionNode(Node):
def __init__(self):
super().__init__('isaac_stereo_perception_node')
self.bridge = CvBridge()
# Stereo camera parameters
self.left_image = None
self.right_image = None
self.left_camera_info = None
self.right_camera_info = None
self.stereo_rectified = False
# ROS 2 subscriptions
self.left_sub = self.create_subscription(
Image,
'/camera/left/image_raw',
self.left_image_callback,
10
)
self.right_sub = self.create_subscription(
Image,
'/camera/right/image_raw',
self.right_image_callback,
10
)
self.left_info_sub = self.create_subscription(
CameraInfo,
'/camera/left/camera_info',
self.left_camera_info_callback,
10
)
self.right_info_sub = self.create_subscription(
CameraInfo,
'/camera/right/camera_info',
self.right_camera_info_callback,
10
)
# Publishers
self.disparity_pub = self.create_publisher(DisparityImage, '/disparity', 10)
self.point_cloud_pub = self.create_publisher(PointStamped, '/point_cloud', 10)
# Stereo processing parameters
self.num_disparities = 64
self.block_size = 11
self.min_disparity = 0
# Initialize stereo matcher
self.stereo = cv2.StereoSGBM_create(
minDisparity=self.min_disparity,
numDisparities=self.num_disparities,
blockSize=self.block_size,
P1=8 * 3 * self.block_size ** 2,
P2=32 * 3 * self.block_size ** 2,
disp12MaxDiff=1,
uniquenessRatio=15,
speckleWindowSize=0,
speckleRange=2,
preFilterCap=63,
mode=cv2.STEREO_SGBM_MODE_SGBM_3WAY
)
self.get_logger().info("Isaac Stereo Perception Node initialized")
def left_image_callback(self, msg):
"""Handle left camera image"""
self.left_image = self.bridge.imgmsg_to_cv2(msg, desired_encoding='passthrough')
self.process_stereo_if_ready()
def right_image_callback(self, msg):
"""Handle right camera image"""
self.right_image = self.bridge.imgmsg_to_cv2(msg, desired_encoding='passthrough')
self.process_stereo_if_ready()
def left_camera_info_callback(self, msg):
"""Handle left camera calibration info"""
self.left_camera_info = msg
self.compute_stereo_rectification()
def right_camera_info_callback(self, msg):
"""Handle right camera calibration info"""
self.right_camera_info = msg
self.compute_stereo_rectification()
def compute_stereo_rectification(self):
"""Compute stereo rectification parameters"""
if self.left_camera_info and self.right_camera_info:
# Extract camera matrices
left_cam_matrix = np.array(self.left_camera_info.k).reshape(3, 3)
right_cam_matrix = np.array(self.right_camera_info.k).reshape(3, 3)
# Extract distortion coefficients
left_dist_coeffs = np.array(self.left_camera_info.d)
right_dist_coeffs = np.array(self.right_camera_info.d)
# Extract rotation and translation between cameras
# This is simplified - in practice, you'd have the extrinsic parameters
R = np.eye(3) # Rotation matrix
T = np.array([-0.1, 0, 0]) # Translation vector (baseline)
# Compute rectification parameters
size = (self.left_camera_info.width, self.left_camera_info.height)
R1, R2, P1, P2, Q, validPixROI1, validPixROI2 = cv2.stereoRectify(
left_cam_matrix, left_dist_coeffs,
right_cam_matrix, right_dist_coeffs,
size, R, T,
flags=cv2.CALIB_ZERO_DISPARITY,
alpha=-1 # Full rectification
)
# Initialize rectification maps
self.left_map1, self.left_map2 = cv2.initUndistortRectifyMap(
left_cam_matrix, left_dist_coeffs, R1, P1, size, cv2.CV_32FC1
)
self.right_map1, self.right_map2 = cv2.initUndistortRectifyMap(
right_cam_matrix, right_dist_coeffs, R2, P2, size, cv2.CV_32FC1
)
self.stereo_rectified = True
self.get_logger().info("Stereo rectification computed successfully")
def process_stereo_if_ready(self):
"""Process stereo images if both images and calibration are available"""
if (self.left_image is not None and
self.right_image is not None and
self.stereo_rectified):
# Rectify images
left_rectified = cv2.remap(
self.left_image, self.left_map1, self.left_map2,
interpolation=cv2.INTER_LINEAR
)
right_rectified = cv2.remap(
self.right_image, self.right_map1, self.right_map2,
interpolation=cv2.INTER_LINEAR
)
# Convert to grayscale
left_gray = cv2.cvtColor(left_rectified, cv2.COLOR_BGR2GRAY)
right_gray = cv2.cvtColor(right_rectified, cv2.COLOR_BGR2GRAY)
# Compute disparity
disparity = self.stereo.compute(left_gray, right_gray).astype(np.float32) / 16.0
# Publish disparity
self.publish_disparity(disparity)
def publish_disparity(self, disparity):
"""Publish disparity image"""
# Create disparity message
disparity_msg = DisparityImage()
disparity_msg.header.stamp = self.get_clock().now().to_msg()
disparity_msg.header.frame_id = "camera_link"
# Set disparity image data
disparity_msg.image = self.bridge.cv2_to_imgmsg(disparity, encoding="32FC1")
disparity_msg.f = 616.3 # Focal length (example value)
disparity_msg.T = 0.1 # Baseline (example value)
disparity_msg.min_disparity = 0.0
disparity_msg.max_disparity = 64.0
disparity_msg.delta_d = 0.125
self.disparity_pub.publish(disparity_msg)
def main(args=None):
rclpy.init(args=args)
stereo_node = IsaacStereoPerceptionNode()
try:
rclpy.spin(stereo_node)
except KeyboardInterrupt:
pass
finally:
stereo_node.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()
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# python/object_detection_pipeline.py
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import Image
from vision_msgs.msg import Detection2DArray, Detection2D, ObjectHypothesisWithPose
from geometry_msgs.msg import Point
from cv_bridge import CvBridge
import numpy as np
import cv2
import torch
from torchvision import transforms
from isaac_ros.detection import IsaacObjectDetector
class IsaacObjectDetectionNode(Node):
def __init__(self):
super().__init__('isaac_object_detection_node')
self.bridge = CvBridge()
# Subscribe to camera image
self.image_sub = self.create_subscription(
Image,
'/camera/rgb/image_raw',
self.image_callback,
10
)
# Publish detections
self.detection_pub = self.create_publisher(Detection2DArray, '/detections', 10)
# Initialize Isaac object detector
self.detector = IsaacObjectDetector(
model_name='detr_resnet50',
confidence_threshold=0.5,
max_detections=20
)
# Class names for COCO dataset (common in Isaac ROS)
self.class_names = [
'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep',
'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella',
'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard',
'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork',
'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',
'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]
self.get_logger().info("Isaac Object Detection Node initialized")
def image_callback(self, msg):
"""Process image and detect objects"""
try:
# Convert ROS Image to OpenCV
cv_image = self.bridge.imgmsg_to_cv2(msg, desired_encoding='passthrough')
# Run object detection
detections = self.detector.detect(cv_image)
# Create detection message
detection_array_msg = Detection2DArray()
detection_array_msg.header = msg.header
# Process detections
for detection in detections:
detection_msg = Detection2D()
# Set header
detection_msg.header = msg.header
# Set bounding box
bbox = detection['bbox']
detection_msg.bbox.size_x = int(bbox['width'])
detection_msg.bbox.size_y = int(bbox['height'])
# Set center point
center_x = int(bbox['x'] + bbox['width'] / 2)
center_y = int(bbox['y'] + bbox['height'] / 2)
detection_msg.bbox.center.x = center_x
detection_msg.bbox.center.y = center_y
detection_msg.bbox.center.z = 0.0 # 2D detection
# Set results
hypothesis = ObjectHypothesisWithPose()
class_id = detection['class_id']
confidence = detection['confidence']
hypothesis.id = str(class_id)
hypothesis.score = confidence
detection_msg.results.append(hypothesis)
# Add class name if available
if class_id < len(self.class_names):
class_name_hypothesis = ObjectHypothesisWithPose()
class_name_hypothesis.id = self.class_names[class_id]
class_name_hypothesis.score = confidence
detection_msg.results.append(class_name_hypothesis)
detection_array_msg.detections.append(detection_msg)
# Publish detections
self.detection_pub.publish(detection_array_msg)
# Log detection results
self.get_logger().debug(f"Published {len(detections)} detections")
except Exception as e:
self.get_logger().error(f"Error in object detection: {e}")
class IsaacObjectDetector:
"""Wrapper for Isaac ROS object detection"""
def __init__(self, model_name='detr_resnet50', confidence_threshold=0.5, max_detections=20):
self.model_name = model_name
self.confidence_threshold = confidence_threshold
self.max_detections = max_detections
# Initialize detection model
self.model = self.load_model(model_name)
def load_model(self, model_name):
"""Load the specified detection model"""
# In practice, this would load the Isaac ROS detection model
# For now, we'll return a placeholder
self.get_logger().info(f"Loading model: {model_name}")
return model_name
def detect(self, image):
"""Run object detection on image"""
# This would interface with Isaac ROS detection packages
# For demonstration, return dummy detections
detections = []
# Simulate detections (in real implementation, this would use the actual model)
# Here we'd typically use Isaac ROS DETR or other detection nodes
if self.model_name == 'detr_resnet50':
# Simulate detection results
# In practice, this would process the image with the actual model
pass
# Return mock detections for demonstration
mock_detections = [
{
'bbox': {'x': 100, 'y': 100, 'width': 200, 'height': 200},
'class_id': 0, # person
'confidence': 0.85
},
{
'bbox': {'x': 400, 'y': 200, 'width': 150, 'height': 150},
'class_id': 56, # chair
'confidence': 0.78
}
]
# Filter based on confidence threshold
detections = [
det for det in mock_detections
if det['confidence'] >= self.confidence_threshold
]
# Limit to max detections
detections = detections[:self.max_detections]
return detections
def main(args=None):
rclpy.init(args=args)
detection_node = IsaacObjectDetectionNode()
try:
rclpy.spin(detection_node)
except KeyboardInterrupt:
pass
finally:
detection_node.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()
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# python/cuda_perception.py
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import Image
from std_msgs.msg import Float32
from cv_bridge import CvBridge
import numpy as np
import time
from numba import cuda
import math
class CudaPerceptionNode(Node):
def __init__(self):
super().__init__('cuda_perception_node')
self.bridge = CvBridge()
# ROS interfaces
self.image_sub = self.create_subscription(
Image,
'/camera/rgb/image_raw',
self.image_callback,
10
)
self.performance_pub = self.create_publisher(Float32, '/cuda_performance', 10)
# Check CUDA availability
self.cuda_available = cuda.is_available()
if self.cuda_available:
self.get_logger().info("CUDA acceleration available")
self.gpu_device = cuda.get_current_device()
self.get_logger().info(f"Using GPU: {self.gpu_device.name}")
else:
self.get_logger().warn("CUDA not available, falling back to CPU")
self.get_logger().info("CUDA Perception Node initialized")
@cuda.jit
def cuda_color_conversion_kernel(self, input_image, output_image):
"""CUDA kernel for RGB to grayscale conversion"""
x, y = cuda.grid(2)
rows, cols = input_image.shape[0], input_image.shape[1]
if x < rows and y < cols:
# Convert RGB to grayscale using luminance formula
r = input_image[x, y, 0]
g = input_image[x, y, 1]
b = input_image[x, y, 2]
gray_value = 0.299 * r + 0.587 * g + 0.114 * b
output_image[x, y] = gray_value
@cuda.jit
def cuda_edge_detection_kernel(self, input_image, output_image):
"""CUDA kernel for simple edge detection"""
x, y = cuda.grid(2)
rows, cols = input_image.shape[0], input_image.shape[1]
if x > 0 and x < rows - 1 and y > 0 and y < cols - 1:
# Simple Sobel edge detection
gx = (input_image[x-1, y-1] + 2*input_image[x, y-1] + input_image[x+1, y-1] -
input_image[x-1, y+1] - 2*input_image[x, y+1] - input_image[x+1, y+1])
gy = (input_image[x-1, y-1] + 2*input_image[x-1, y] + input_image[x-1, y+1] -
input_image[x+1, y-1] - 2*input_image[x+1, y] - input_image[x+1, y+1])
magnitude = math.sqrt(gx*gx + gy*gy)
output_image[x, y] = min(255.0, magnitude)
def image_callback(self, msg):
"""Process image using CUDA acceleration"""
try:
start_time = time.time()
# Convert ROS Image to OpenCV
cv_image = self.bridge.imgmsg_to_cv2(msg, desired_encoding='passthrough')
if not self.cuda_available:
# Fallback to CPU processing
self.cpu_processing(cv_image)
return
# Transfer image to GPU
gpu_image = cuda.to_device(cv_image)
# Prepare output arrays
gray_image = np.zeros((cv_image.shape[0], cv_image.shape[1]), dtype=np.float32)
gpu_gray = cuda.to_device(gray_image)
# Configure CUDA grid
threads_per_block = (16, 16)
blocks_per_grid_x = math.ceil(cv_image.shape[0] / threads_per_block[0])
blocks_per_grid_y = math.ceil(cv_image.shape[1] / threads_per_block[1])
blocks_per_grid = (blocks_per_grid_x, blocks_per_grid_y)
# Run color conversion kernel
self.cuda_color_conversion_kernel[blocks_per_grid, threads_per_block](
gpu_image, gpu_gray
)
# Copy result back to host
result_gray = gpu_gray.copy_to_host()
# Perform edge detection on grayscale image
edge_image = np.zeros_like(result_gray)
gpu_edge = cuda.to_device(edge_image)
self.cuda_edge_detection_kernel[blocks_per_grid, threads_per_block](
gpu_gray, gpu_edge
)
result_edge = gpu_edge.copy_to_host()
# Measure performance
processing_time = time.time() - start_time
# Publish performance metric
perf_msg = Float32()
perf_msg.data = 1.0 / processing_time if processing_time > 0 else 0.0
self.performance_pub.publish(perf_msg)
self.get_logger().debug(f"CUDA processing time: {processing_time:.3f}s, FPS: {perf_msg.data:.1f}")
except Exception as e:
self.get_logger().error(f"Error in CUDA processing: {e}")
def cpu_processing(self, image):
"""CPU fallback for image processing"""
start_time = time.time()
# Convert to grayscale using CPU
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Apply edge detection
edges = cv2.Canny(gray, 50, 150)
processing_time = time.time() - start_time
# Publish performance metric
perf_msg = Float32()
perf_msg.data = 1.0 / processing_time if processing_time > 0 else 0.0
self.performance_pub.publish(perf_msg)
self.get_logger().debug(f"CPU processing time: {processing_time:.3f}s, FPS: {perf_msg.data:.1f}")
def main(args=None):
rclpy.init(args=args)
cuda_node = CudaPerceptionNode()
try:
rclpy.spin(cuda_node)
except KeyboardInterrupt:
pass
finally:
cuda_node.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()
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# python/nitros_pipeline.py
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import Image, CameraInfo
from std_msgs.msg import String
from cv_bridge import CvBridge
import numpy as np
from isaac_ros.nitros import NitrosType, NitrosPublisher, NitrosSubscriber
class NitrosPerceptionPipelineNode(Node):
def __init__(self):
super().__init__('nitros_perception_pipeline_node')
self.bridge = CvBridge()
# Initialize NITROS types for optimized data transport
self.nitros_image_type = NitrosType(
name='nitros_image_bgr8',
supported_types=['sensor_msgs/msg/Image']
)
self.nitros_camera_info_type = NitrosType(
name='nitros_camera_info',
supported_types=['sensor_msgs/msg/CameraInfo']
)
# Create NITROS subscribers
self.image_sub = NitrosSubscriber(
self,
self.nitros_image_type,
'camera/rgb/image_raw',
qos_profile=10
)
self.camera_info_sub = NitrosSubscriber(
self,
self.nitros_camera_info_type,
'camera/rgb/camera_info',
qos_profile=10
)
# Create NITROS publisher
self.processed_image_pub = NitrosPublisher(
self,
self.nitros_image_type,
'camera/rgb/processed_image',
qos_profile=10
)
# Synchronize topics using NITROS
self.image_sub.registerCallback(self.image_callback)
self.camera_info_sub.registerCallback(self.camera_info_callback)
# Processing state
self.camera_matrix = None
self.latest_image = None
self.get_logger().info("NITROS Perception Pipeline initialized")
def image_callback(self, msg):
"""Handle incoming image message"""
try:
# Convert to OpenCV
cv_image = self.bridge.imgmsg_to_cv2(msg, desired_encoding='passthrough')
# Store latest image for processing
self.latest_image = cv_image
# Process image if camera calibration is available
if self.camera_matrix is not None:
processed_image = self.process_image_with_calibration(cv_image)
else:
processed_image = self.process_image(cv_image)
# Publish processed image
processed_msg = self.bridge.cv2_to_imgmsg(processed_image, encoding='bgr8')
processed_msg.header = msg.header
self.processed_image_pub.publish(processed_msg)
except Exception as e:
self.get_logger().error(f"Error processing image: {e}")
def camera_info_callback(self, msg):
"""Handle camera calibration information"""
self.camera_matrix = np.array(msg.k).reshape(3, 3)
self.distortion_coeffs = np.array(msg.d)
self.get_logger().info(f"Camera calibration updated: {self.camera_matrix.shape}")
def process_image_with_calibration(self, image):
"""Process image with camera calibration"""
# Undistort image using calibration parameters
undistorted = cv2.undistort(
image,
self.camera_matrix,
self.distortion_coeffs,
None,
self.camera_matrix
)
# Apply some processing (e.g., enhance features)
processed = self.enhance_features(undistorted)
return processed
def process_image(self, image):
"""Process image without calibration"""
# Apply basic processing
processed = self.enhance_features(image)
return processed
def enhance_features(self, image):
"""Enhance image features for better perception"""
# Convert to LAB color space for better feature enhancement
lab = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
# Enhance the L channel
l, a, b = cv2.split(lab)
# Apply CLAHE to L channel
clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
l = clahe.apply(l)
# Merge channels back
enhanced_lab = cv2.merge([l, a, b])
# Convert back to BGR
enhanced = cv2.cvtColor(enhanced_lab, cv2.COLOR_LAB2BGR)
return enhanced
def main(args=None):
rclpy.init(args=args)
nitros_node = NitrosPerceptionPipelineNode()
try:
rclpy.spin(nitros_node)
except KeyboardInterrupt:
pass
finally:
nitros_node.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()
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# python/perception_action_coupling.py
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import Image, JointState
from geometry_msgs.msg import Twist, PoseStamped
from std_msgs.msg import String
from cv_bridge import CvBridge
import numpy as np
import cv2
from scipy.spatial.transform import Rotation as R
class PerceptionActionCouplingNode(Node):
def __init__(self):
super().__init__('perception_action_coupling_node')
self.bridge = CvBridge()
# Robot state
self.joint_states = None
self.robot_pose = None
# Perception data
self.latest_image = None
self.object_detections = []
# ROS interfaces
self.image_sub = self.create_subscription(
Image,
'/camera/rgb/image_raw',
self.image_callback,
10
)
self.joint_state_sub = self.create_subscription(
JointState,
'/joint_states',
self.joint_state_callback,
10
)
self.pose_sub = self.create_subscription(
PoseStamped,
'/vslam_pose',
self.pose_callback,
10
)
# Robot control publishers
self.cmd_vel_pub = self.create_publisher(Twist, '/cmd_vel', 10)
self.joint_cmd_pub = self.create_publisher(JointState, '/joint_commands', 10)
# State tracking
self.robot_mode = "exploring" # exploring, tracking, manipulating
self.target_object = None
self.get_logger().info("Perception-Action Coupling Node initialized")
def image_callback(self, msg):
"""Process image and detect objects for action planning"""
try:
# Convert to OpenCV
cv_image = self.bridge.imgmsg_to_cv2(msg, desired_encoding='passthrough')
self.latest_image = cv_image
# Run object detection (simplified)
detections = self.detect_objects(cv_image)
self.object_detections = detections
# Update target based on detections
self.update_target(detections)
# Plan actions based on perception
self.plan_actions()
except Exception as e:
self.get_logger().error(f"Error in image processing: {e}")
def detect_objects(self, image):
"""Detect objects in image"""
# Simplified object detection
# In practice, this would use Isaac ROS detection nodes
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Detect circles as simple objects
circles = cv2.HoughCircles(
gray,
cv2.HOUGH_GRADIENT,
dp=1,
minDist=50,
param1=50,
param2=30,
minRadius=10,
maxRadius=100
)
detections = []
if circles is not None:
circles = np.round(circles[0, :]).astype("int")
for (x, y, r) in circles:
detections.append({
'center': (x, y),
'radius': r,
'bbox': (x-r, y-r, 2*r, 2*r),
'class': 'circle_object',
'confidence': 0.8
})
return detections
def joint_state_callback(self, msg):
"""Update robot joint states"""
self.joint_states = msg
def pose_callback(self, msg):
"""Update robot pose from VSLAM"""
self.robot_pose = msg
def update_target(self, detections):
"""Update target object based on detections"""
if detections:
# For simplicity, target the first detected object
# In practice, this would use more sophisticated targeting logic
self.target_object = detections[0]
# Determine action based on object type
if detections[0]['class'] == 'circle_object':
self.robot_mode = "tracking"
def plan_actions(self):
"""Plan robot actions based on perception"""
if self.target_object is not None and self.robot_pose is not None:
# Calculate relative position to target
image_center = (320, 240) # Assuming 640x480 image
target_center = self.target_object['center']
# Calculate angular offset
dx = target_center[0] - image_center[0]
dy = target_center[1] - image_center[1]
# Normalize to [-1, 1] range
norm_dx = dx / (image_center[0]) # 320 is half image width
norm_dy = dy / (image_center[1]) # 240 is half image height
# Convert to robot commands
cmd = Twist()
# Proportional control for turning toward target
cmd.angular.z = -norm_dx * 0.5 # Negative for correct direction
# Forward movement if target is centered
if abs(norm_dx) < 0.1: # If roughly centered
cmd.linear.x = min(0.2, 0.1 * (1 - abs(norm_dy))) # Move forward if not too close
# Publish command
self.cmd_vel_pub.publish(cmd)
def execute_manipulation(self):
"""Execute manipulation action if target is close enough"""
if self.target_object is not None:
# Check if target is within reach
# This would involve more complex 3D position estimation
# and inverse kinematics
# For demonstration, we'll simulate joint commands
if self.joint_states is not None:
cmd_msg = JointState()
cmd_msg.header.stamp = self.get_clock().now().to_msg()
cmd_msg.name = self.joint_states.name # Use same joint names
cmd_msg.position = list(self.joint_states.position) # Start with current positions
# Modify joint positions for reaching
# This is simplified - real implementation would use inverse kinematics
if 'right_shoulder_joint' in cmd_msg.name:
idx = cmd_msg.name.index('right_shoulder_joint')
cmd_msg.position[idx] += 0.1 # Move shoulder up
if 'right_elbow_joint' in cmd_msg.name:
idx = cmd_msg.name.index('right_elbow_joint')
cmd_msg.position[idx] += 0.1 # Bend elbow
self.joint_cmd_pub.publish(cmd_msg)
def main(args=None):
rclpy.init(args=args)
coupling_node = PerceptionActionCouplingNode()
try:
rclpy.spin(coupling_node)
except KeyboardInterrupt:
pass
finally:
coupling_node.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()
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# python/performance_benchmarking.py
import rclpy
from rclpy.node import Node
from std_msgs.msg import Float32, Int32
from sensor_msgs.msg import Image
import time
import numpy as np
from collections import deque
class IsaacPerfBenchmarkNode(Node):
def __init__(self):
super().__init__('isaac_perf_benchmark_node')
# Performance tracking
self.fps_history = deque(maxlen=100)
self.processing_times = deque(maxlen=100)
self.memory_usage = deque(maxlen=100)
# Publishers
self.fps_pub = self.create_publisher(Float32, '/performance/fps', 10)
self.processing_time_pub = self.create_publisher(Float32, '/performance/processing_time', 10)
self.load_pub = self.create_publisher(Float32, '/performance/load', 10)
# Subscriber for performance monitoring
self.image_sub = self.create_subscription(
Image,
'/camera/rgb/image_raw',
self.image_callback,
10
)
# Timer for publishing metrics
self.timer = self.create_timer(1.0, self.publish_performance_metrics)
# Performance counters
self.frame_count = 0
self.last_time = time.time()
self.get_logger().info("Isaac Performance Benchmark Node initialized")
def image_callback(self, msg):
"""Process image and track performance"""
start_time = time.time()
# Simulate image processing time
# In real implementation, this would be the actual processing
time.sleep(0.01) # Simulate 10ms processing time
processing_time = time.time() - start_time
self.processing_times.append(processing_time)
# Update frame count
self.frame_count += 1
# Calculate FPS
current_time = time.time()
if current_time - self.last_time >= 1.0:
fps = self.frame_count / (current_time - self.last_time)
self.fps_history.append(fps)
self.frame_count = 0
self.last_time = current_time
def publish_performance_metrics(self):
"""Publish performance metrics"""
if self.fps_history:
avg_fps = np.mean(self.fps_history)
avg_processing_time = np.mean(self.processing_times) if self.processing_times else 0.0
# Calculate load (0.0 to 1.0) - higher FPS is better, lower processing time is better
max_target_fps = 30.0 # Target FPS
load = min(1.0, avg_processing_time * max_target_fps)
# Publish metrics
fps_msg = Float32()
fps_msg.data = float(avg_fps)
self.fps_pub.publish(fps_msg)
proc_time_msg = Float32()
proc_time_msg.data = float(avg_processing_time * 1000) # Convert to milliseconds
self.processing_time_pub.publish(proc_time_msg)
load_msg = Float32()
load_msg.data = float(load)
self.load_pub.publish(load_msg)
# Log performance
self.get_logger().info(
f"Performance: {avg_fps:.1f} FPS, "
f"{avg_processing_time*1000:.1f}ms, "
f"Load: {load:.2f}"
)
class IsaacPerfAnalyzer:
"""Performance analyzer for Isaac ROS pipelines"""
def __init__(self):
self.metrics = {
'latency': [],
'throughput': [],
'memory_usage': [],
'cpu_usage': [],
'gpu_usage': []
}
def measure_latency(self, input_time, output_time):
"""Measure processing latency"""
latency = output_time - input_time
self.metrics['latency'].append(latency)
return latency
def measure_throughput(self, num_operations, time_interval):
"""Measure processing throughput"""
throughput = num_operations / time_interval
self.metrics['throughput'].append(throughput)
return throughput
def analyze_performance(self):
"""Analyze performance metrics"""
analysis = {}
if self.metrics['latency']:
analysis['avg_latency'] = np.mean(self.metrics['latency'])
analysis['max_latency'] = np.max(self.metrics['latency'])
analysis['p95_latency'] = np.percentile(self.metrics['latency'], 95)
if self.metrics['throughput']:
analysis['avg_throughput'] = np.mean(self.metrics['throughput'])
analysis['min_throughput'] = np.min(self.metrics['throughput'])
return analysis
def get_optimization_recommendations(self):
"""Get recommendations for performance optimization"""
analysis = self.analyze_performance()
recommendations = []
if analysis.get('avg_latency', 0) > 0.1: # 100ms threshold
recommendations.append("High latency detected - consider reducing processing complexity or increasing hardware resources")
if analysis.get('avg_throughput', 0) < 10: # 10 ops/sec threshold
recommendations.append("Low throughput - consider optimizing algorithms or using more efficient data structures")
return recommendations
def main(args=None):
rclpy.init(args=args)
perf_node = IsaacPerfBenchmarkNode()
try:
rclpy.spin(perf_node)
except KeyboardInterrupt:
pass
finally:
perf_node.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()
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