How computer vision is used in robot navigation?

Computer vision enables robots to navigate by processing visual data to understand their environment, locate themselves, and plan paths. It typically involves cameras or depth sensors capturing images, which algorithms analyze to detect obstacles, track movement, and build maps. For example, a robot might use stereo cameras to estimate distances to objects or apply machine learning models to recognize navigable surfaces. This visual input is combined with other sensors (like lidar or IMUs) to create a reliable navigation system.

One key application is localization and mapping. Robots use techniques like Simultaneous Localization and Mapping (SLAM), where computer vision identifies environmental features (e.g., edges, corners, or textures) to track the robot’s position while building a map. For instance, ORB-SLAM employs feature extraction to match landmarks across successive camera frames, allowing the robot to estimate its movement and update the map in real time. Drones often use optical flow—a computer vision method that detects pixel motion between frames—to stabilize and navigate in GPS-denied environments. These approaches help robots operate in unknown or dynamic spaces, like warehouses or outdoor terrain, without relying solely on preloaded maps.

Another critical use is obstacle detection and path planning. Computer vision models like YOLO (You Only Look Once) can identify pedestrians, vehicles, or debris in real time, enabling robots like delivery bots or autonomous cars to react dynamically. Depth sensors (e.g., RGB-D cameras) provide 3D data to calculate obstacle distances, which pathfinding algorithms like A* or RRT* use to plot safe routes. For example, agricultural robots might segment crops from weeds using semantic segmentation, adjusting their path to avoid damaging plants. In indoor settings, vacuum robots combine edge detection to follow walls and object recognition to avoid furniture. By processing visual data efficiently (often via frameworks like ROS or OpenCV), robots balance accuracy and speed, ensuring reliable navigation even in cluttered or changing environments.