Robot Perception

Build perception systems that enable robots to understand their environment through computer vision, LiDAR processing, and multi-sensor fusion.

Why Perception Matters

Perception is the robot's window to the world. Without robust perception, a robot cannot navigate safely, manipulate objects, or interact with humans. Modern AI-powered perception systems combine multiple sensor modalities and deep learning to achieve human-level (or better) understanding of the environment.

Sensor Modalities

Computer Vision with Deep Learning

import cv2
import torch
from ultralytics import YOLO

# Load a pre-trained YOLOv8 model for object detection
model = YOLO('yolov8n.pt')

# Process camera feed
cap = cv2.VideoCapture(0)
while True:
    ret, frame = cap.read()
    if not ret:
        break

    # Run inference
    results = model(frame)

    # Draw detections
    annotated = results[0].plot()
    cv2.imshow('Robot Vision', annotated)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

SLAM (Simultaneous Localization and Mapping)

SLAM allows a robot to build a map of its environment while simultaneously tracking its own position within that map. Key SLAM approaches include:

• Visual SLAM (ORB-SLAM3): Uses camera features for mapping and localization
• LiDAR SLAM (Cartographer): Uses point clouds for high-accuracy 2D/3D mapping
• Visual-Inertial SLAM (VINS-Fusion): Combines camera and IMU for robust tracking
• RGB-D SLAM (RTAB-Map): Uses depth cameras for dense 3D reconstruction

Sensor Fusion

No single sensor is perfect. Sensor fusion combines data from multiple sources to produce more accurate and reliable perception: