FAST-LIO2 vs LIO-SAM: A Head-to-Head Technical Comparison

FAST-LIO2 excels at raw computational speed and robustness in chaotic environments because of its tightly-coupled, iterated extended Kalman filter (IEKF) architecture. For example, it consistently achieves odometry computation at 50-100 Hz directly on a live LiDAR point stream without feature extraction, making it a top choice for aggressive drone flight where a single state estimation delay can cause a crash.

LIO-SAM takes a different approach by leveraging a factor graph optimization backend (GTSAM) that performs batch smoothing over a sliding window of keyframes. This results in significantly higher global map consistency and trajectory smoothness, but at the cost of higher computational load and a dependency on feature-rich environments for reliable loop closure detection.

The key trade-off: If your priority is high-speed, computationally efficient state estimation for a fast-moving platform like a racing drone, choose FAST-LIO2. If you prioritize globally consistent, survey-grade 3D maps for applications like construction site documentation or autonomous driving, choose LIO-SAM. The decision hinges on whether you need instantaneous odometry or a globally refined map as the final product.

[FAST-LIO2] excels at raw computational speed and state estimation robustness because it leverages an iterated extended Kalman filter (IEKF) directly on raw LiDAR points without feature extraction. For example, in benchmark tests on an Intel i7-12700H CPU, FAST-LIO2 consistently achieves odometry computation times of 1.5–2.5 ms per scan, enabling a pipeline rate of over 400 Hz. This makes it the definitive choice for high-speed drone flight or agile ground robots where a control loop demands minimal latency and the system cannot afford the overhead of feature-based matching.

[LIO-SAM] takes a different approach by performing factor graph optimization over a sliding window of keyframes. This strategy results in a trade-off where computational load increases with trajectory length, but the output is a globally consistent trajectory and map with effectively zero drift in revisiting scenarios. In structured environments like warehouses, LIO-SAM's loop closure detection can reduce absolute trajectory error (ATE) to sub-5 cm over kilometer-scale runs, a level of precision that pure filtering methods struggle to maintain without explicit loop correction.

The key trade-off: If your priority is computational efficiency, low-latency control feedback, and deployment on resource-constrained embedded hardware, choose FAST-LIO2. If you prioritize survey-grade map quality, long-term global consistency, and the ability to correct historical drift via loop closures, choose LIO-SAM. For applications like high-speed exploration, FAST-LIO2 is the superior local estimator; for applications like construction site digital twins or final 3D asset generation, LIO-SAM's smoothing backend is indispensable.