What is sensor fusion in robotics?

Sensor fusion in robotics refers to the process of combining data from multiple sensors to create a more accurate and reliable understanding of the environment. By integrating inputs from different sensor types—such as cameras, LiDAR, radar, IMUs (Inertial Measurement Units), or ultrasonic sensors—robots can compensate for individual sensor limitations and reduce uncertainty. For example, a camera might provide high-resolution color images but struggle in low light, while LiDAR offers precise depth information but lacks texture details. Fusing these data streams allows the robot to maintain robust perception across varying conditions, improving tasks like object detection, navigation, or localization.

The technical implementation often involves algorithms that align, filter, and merge sensor data. Common approaches include Kalman filters (for linear systems), particle filters (for non-linear or probabilistic scenarios), or Bayesian networks. For instance, in a self-driving car, a Kalman filter might combine GPS data (low update rate but absolute position) with IMU measurements (high-frequency but prone to drift) to estimate the vehicle’s position smoothly. More advanced systems use machine learning models, such as neural networks, to process raw sensor inputs and output fused representations. Sensor fusion can occur at different levels: “low-level” fusion combines raw data (e.g., merging LiDAR and camera pixels), while “high-level” fusion integrates processed outputs (e.g., combining object detections from separate sensors).

Practical applications highlight both the benefits and challenges. Autonomous drones, for example, fuse visual odometry (from cameras) with IMU data to stabilize flight when GPS signals are lost. However, sensor fusion introduces complexity: timing synchronization between sensors, handling conflicting data (e.g., a camera sees an obstacle but radar does not), and computational overhead. Developers must also account for sensor calibration errors and environmental noise. Despite these challenges, sensor fusion remains critical for building robust robotic systems, as no single sensor can provide all the necessary information reliably. Tools like ROS (Robot Operating System) offer libraries for sensor alignment and fusion, simplifying implementation for developers working on perception pipelines.