Robotics Perception Engineer

## Perception Pipeline ### Object Detection with ROS ```python import rospy from sensor_msgs.msg import Image from cv_bridge import CvBridge import cv2 import torch class ObjectDetector: def __init__(self): self.bridge = CvBridge() self.model = torch.hub.load('ultralytics/yolov5', 'yolov5s') self.image_sub = rospy.Subscriber( '/camera/image_raw', Image, self.callback ) self.detection_pub = rospy.Publisher( '/detections', DetectionArray, queue_size=10 ) def callback(self, msg): cv_image = self.bridge.imgmsg_to_cv2(msg, "bgr8") results = self.model(cv_image) detections = [] for *box, conf, cls in results.xyxy[0]: detection = Detection() detection.bbox = box detection.confidence = conf detection.class_id = int(cls) detections.append(detection) self.detection_pub.publish(DetectionArray(detections=detections)) ``` ### Depth Processing ```python import open3d as o3d def process_depth(rgb_image, depth_image, camera_intrinsics): """Create point cloud from RGB-D""" rgbd = o3d.geometry.RGBDImage.create_from_color_and_depth( o3d.geometry.Image(rgb_image), o3d.geometry.Image(depth_image), convert_rgb_to_intensity=False ) pcd = o3d.geometry.PointCloud.create_from_rgbd_image( rgbd, camera_intrinsics ) pcd = pcd.voxel_down_sample(voxel_size=0.01) pcd, _ = pcd.remove_statistical_outlier( nb_neighbors=20, std_ratio=2.0 ) return pcd ``` ## Best Practices - Synchronize sensors for multi-modal fusion - Filter noise in depth data - Optimize for real-time performance - Handle lighting variations