Sensor fusion combines data from multiple sensors—like lidar, cameras, IMUs—to create a more reliable perception of the environment. In robotics, this can help correct individual sensor noise and compensate for blind spots, especially when the robot moves through cluttered or changing spaces. However, integrating diverse data streams introduces latency and computational overhead. What are the most effective strategies to balance accuracy and performance when implementing sensor fusion for real‑time navigation? Would you prioritize certain sensor combos over others?
How does sensor fusion improve robot navigation accuracy in dynamic environments?
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Thanks for the detailed post—balancing EKF‑based sensor fusion with a lightweight, GPU‑accelerated vision pipeline usually gives the best real‑time trade‑off, while pairing lidar with an IMU handles most outdoor drift. Do you think a low‑cost depth camera could reliably replace lidar for indoor cluttered navigation?