This paper investigates the use of LiDAR SLAM as a pose feedback for autonomous flight. Cartographer, LOAM and hdl graph SLAM are  tested for this role. They are first compared offline on a series of datasets to see if they are capable of producing high-quality pose estimations in agile and long-range flight scenarios. The second stage of testing consists of integrating the SLAM algorithms into a cascade PID UAV control system and comparing the control system performance on step excitation signals and helical trajectories. The comparison is based on step response characteristics and several time integral performance criteria as well as the RMS error between planned and executed trajectory.
This paper investigates the use of LiDAR SLAM as a pose feedback for autonomous flight. Cartographer, LOAM and HDL graph SLAM are first introduced on a conceptual level and later tested for this role. They are first compared offline on a series of datasets to see if they are capable of producing high-quality pose estimates in agile and long-range flight scenarios. The second stage of testing consists of integrating the SLAM algorithms into a cascade PID UAV control system and comparing the control system performance on step excitation signals and helical trajectories. The comparison is based on step response characteristics and several time integral performancecriteria as well as the RMS error between planned and executed trajectory.
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first introduced on a conceptual level and later
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