robot-perception

Setting up a multi-sensor perception pipeline in ROS2 involves configuring cameras, LiDARs, and depth sensors, then establishing synchronized data streaming and fusion workflows for mapping and object detection.

Hand-eye calibration determines the spatial transform between a robot's end-effector and a mounted camera. You need it for visual servoing tasks to ensure the robot accurately interacts with objects identified by the perception pipeline.

Yes, you can fuse LiDAR point clouds with camera images. Multi-sensor fusion workflows combine depth data and RGB images to provide richer environmental understanding for localization and mapping tasks.

The best way to debug latency in a robot perception stack is through latency budgeting, which analyzes the processing time of each sensor streaming and fusion component to identify bottlenecks before production deployment.

Yes, production deployment readiness requires multi-sensor synchronization. Aligning timestamps across cameras, LiDARs, and IMUs ensures accurate fusion and prevents tracking errors during real-time robotic operations.

Extrinsic calibration on depth sensors often fails due to poor multi-sensor synchronization or insufficient overlap in the sensor fields of view, which disrupts the accurate spatial transform calculations needed for fusion.