Executive Summary
Mastering warehouse AMR fleets: configuring 3D LiDAR SLAM with FAST-LIO2 and deploying ROS 2 Nav2 lifecycle nodes for industrial logistics.
Key Takeaways
- ✓FAST-LIO2: Fast LiDAR-Inertial Odometry — covered in depth with practical examples, formulas, and code.
- ✓Nav2 Lifecycle Architecture — covered in depth with practical examples, formulas, and code.
Autonomous Mobile Robots (AMRs) in Logistics: Nav2 & 3D LiDAR SLAM
E-commerce fulfillment centers require fleets of dozens of AMRs moving pallets at 2.5 m/s without stopping or colliding. Traditional 2D planar LiDAR struggles in dynamic warehouses where pallet heights change continuously. Modern AMRs use solid-state 3D LiDAR + FAST-LIO2 SLAM.
#1. FAST-LIO2: Fast LiDAR-Inertial Odometry
FAST-LIO2 direct odometry computes sub-centimeter poses by fusing high-rate IMU measurements (200 Hz) with raw 3D point cloud points (10 Hz) using an iterated extended Kalman filter (iEKF):
[ 3D LiDAR (Livox / Ouster) ] ──Raw Points──┐
├──> [ iEKF State Estimator (FAST-LIO2) ] ──> 6-DoF Odometry
[ High-Precision IMU (6-axis) ] ──Delta-V───┘Unlike classic ICP (Iterative Closest Point), FAST-LIO2 maintains an incremental kd-tree (ikd-Tree), updating point environments in under 12 milliseconds.
#2. Nav2 Lifecycle Architecture
In production environments, navigation nodes must be deterministically controlled. ROS 2 Nav2 uses managed lifecycle states:
If an AMR's emergency bumper triggers, the global planner and controller nodes transition from Active to Inactive within 2 milliseconds, cutting motor power safely.