What are the most common sensors used in robotics (e.g., cameras, LIDAR, IMUs)?

Robotics relies on a core set of sensors to perceive environments, navigate, and interact with objects. The most widely used include cameras, LIDAR (Light Detection and Ranging), and IMUs (Inertial Measurement Units), each serving distinct purposes. Cameras capture visual data for tasks like object recognition or navigation, while LIDAR creates precise 3D maps using laser pulses. IMUs track motion through accelerometers and gyroscopes, providing real-time orientation and velocity data. Additional sensors like ultrasonic rangefinders, force-torque sensors, and tactile sensors are also common but often secondary to the primary trio.

Cameras are versatile, ranging from simple RGB sensors to depth-sensing variants like stereo or time-of-flight (ToF) cameras. For example, industrial robots use RGB-D cameras to identify objects on conveyor belts, while autonomous drones rely on stereo vision for obstacle avoidance. LIDAR systems vary in resolution and range—2D LIDAR is used for basic floor mapping in vacuum robots, while 3D LIDAR in self-driving cars generates detailed point clouds for localization. IMUs, often paired with GPS, correct positional drift in mobile robots. A typical IMU combines a three-axis accelerometer (measuring linear motion) and a gyroscope (tracking rotational velocity), enabling drones to stabilize mid-flight or robotic arms to adjust grip during manipulation.

These sensors are rarely used alone. Sensor fusion—combining data from multiple sources—is critical for accuracy. For instance, autonomous vehicles merge LIDAR, cameras, and IMUs to cross-validate obstacles and reduce errors from sensor limitations (e.g., LIDAR struggling in fog). Developers often use frameworks like ROS (Robot Operating System) to synchronize sensor inputs and apply algorithms like Kalman filters for smoother motion tracking. Practical challenges include managing computational load, calibrating sensors, and handling environmental interference (e.g., glare affecting cameras). By integrating these sensors effectively, robots achieve robust perception, balancing cost, power, and performance for their specific use cases.