Benchmarking LiDAR and LiDAR-Inertial Odometry Algorithms for Embedded UAV Deployment in Warehouse Environments

Reliable LiDAR-inertial odometry is central to UAV navigation in GNSS-denied indoor logistics, yet most benchmarks target ground vehicles and ignore the compute limits of aerial platforms. We benchmark six systems spanning two modalities and three estimator families—LiDAR-only (F-LOAM, KISS-ICP) and, within tightly coupled LiDAR-inertial estimation, filter-based (FAST-LIO2, Faster-LIO), graph-optimisation-based (liorf, from LIO-SAM) and geometric-observer-based (DLIO)—on flight data from a motion-capture laboratory and an operational warehouse. We identify and correct four ways a per-scan latency figure misleads; one misstates a system’s cost fourfold. When identical data are replayed on a workstation and on the vehicle’s computer, accuracy transfers to within 3 mm, while cost does not: the penalty tracks how far an implementation distributes its work, and on the passively cooled target it moves by up to a factor of two between sessions whose trajectories agree within millimetres. The workstation’s most accurate system collapses on the target, silently discarding up to two-thirds of its input while latency, coverage and memory report normal operation. Of the two systems that stay inexpensive, within budget and consistent with the reference, Faster-LIO is the cheaper; used as the vehicle’s position source, it reduced the onboard visual-inertial estimator’s typical error on all eight closed-loop flights.