Computer vision and SLAM (Simultaneous Localization and Mapping) are related but distinct fields within robotics and AI. Computer vision focuses on enabling machines to interpret visual data, such as images or videos, to recognize objects, detect patterns, or understand scenes. SLAM, on the other hand, is a specific technique used by robots or autonomous systems to build a map of an unknown environment while simultaneously tracking their own position within that map. While both rely on processing visual or sensor data, their goals differ: computer vision aims to extract meaning from visual inputs, whereas SLAM solves the geometric problem of navigation and spatial awareness.
The core techniques and applications of these fields also diverge. Computer vision uses algorithms like convolutional neural networks (CNNs) for tasks such as image classification (e.g., identifying a cat in a photo) or object detection (e.g., locating pedestrians in a self-driving car’s camera feed). SLAM combines data from sensors like cameras, LiDAR, or inertial measurement units (IMUs) to estimate movement and construct a 3D map in real time. For example, a drone using SLAM might process camera frames to detect feature points in a room, track how those points shift as it moves, and use that data to infer its trajectory and update the map. Computer vision might be used within SLAM for feature detection, but SLAM adds the layer of fusing sensor data to solve localization and mapping together.
While there is overlap, the use cases often differ. Computer vision has broad applications beyond robotics, such as medical imaging analysis, facial recognition, or augmented reality filters. SLAM is primarily used in robotics, autonomous vehicles, or AR/VR systems where spatial understanding is critical. For instance, a warehouse robot might use SLAM to navigate shelves, while a computer vision system in the same robot could identify misplaced items. SLAM systems often depend on computer vision techniques (e.g., optical flow), but they require additional components like pose estimation algorithms and sensor fusion to handle the challenges of real-time mapping and localization. In summary, computer vision provides tools to “see,” while SLAM uses those tools to “navigate and map.”