Comparison of Visual and LiDAR SLAM Algorithms using NASA Flight Test Data

Simultaneous Localization and Mapping (SLAM) is a promising technique that provides
localization information and precise mapping of the physical environment without having much
prior knowledge of the surroundings. SLAM may have a vital role in aeronautics and aerospace,
where vehicles and aircraft must operate in complex environments with traditional localization
services that may be degraded or unavailable. This paper compares several pre-canned 3D
SLAM algorithms based on vision and LiDAR, namely ORB-SLAM, ORB-SLAM2, LOAM,
A-LOAM, and F-LOAM on NASA UAS (Unmanned Aircraft System) flight test data. The
NASA ARC UAS flight test demonstrates preliminary SLAM algorithm results, which serve
as a stepping stone to simulated AAM (Advanced Air Mobility) concepts. Conducting AFRC
UAS flight test for simulated AAM approach and landing with SLAM algorithms provides
an Alternative Precision Navigation and Timing solution based on distributed landmarks and
fiducials in the landing zone. These algorithms use the telemetry data as ground truth for a
baseline comparison. The criteria of the performance comparison include robustness, accuracy,
re-localization, response to environmental changes, and real-time effectiveness, which are
currently qualitative but to be quantitative in the future.