A distributed approach for lidar-based relative state estimation of multi-UAV in GPS-denied environments

In this paper,we present a distributed framework for the lidar-based relative state estimator which achieves highly accurate,real-time trajectory estimation of multiple Unmanned Aerial Vehicles(UAVs)in GPS-denied environments.The system builds atop a factor graph,and only on-board sensors and computing power are utilized.Benefiting from the keyframe strategy,each UAV performs relative state estimation individually and broadcasts very partial information without exchanging raw data.The complete system runs in real-time and is evaluated with three experiments in different environments.Experimental results show that the proposed distributed approach offers comparable performance with a centralized method in terms of accuracy and real-time performance.The flight test demonstrates that the proposed relative state estimation framework is able to be used for aggressive flights over 5 m/s.

Fusing Digital Elevation Models to Improve Hydrological Interpretations

Improving the accuracy of digital elevation is essential for reducing hydro-topographic derivation errors pertaining to, e.g., flow direction, basin borders, channel networks, depressions, flood forecasting, and soil drainage. This article demonstrates how a gain in this accuracy is improved through digital elevation model (DEM) fusion, and using LiDAR-derived elevation layers for conformance testing and validation. This demonstration is done for the Province of New Brunswick (NB, Canada), using five province-wide DEM sources (SRTM 90 m;SRTM 30 m;ASTER 30 m;CDED 22 m;NB-DEM 10 m) and a five-stage process that guides the re-projection of these DEMs while minimizing their elevational differences relative to LiDAR-captured bare-earth DEMs, through calibration and validation. This effort decreased the resulting non-LiDAR to LiDAR elevation differences by a factor of two, reduced the minimum distance conformance between the non-LiDAR and LiDAR-derived flow channels to ± 10 m at 8.5 times out of 10, and dropped the non-LiDAR wet-area percentages of false positives from 59% to 49%, and of false negatives from 14% to 7%. While these reductions are modest, they are nevertheless not only consistent with already existing hydrographic data layers informing about stream and wet-area locations, they also extend these data layers across the province by comprehensively locating previously unmapped flow channels and wet areas.