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Method for accuracy assessment of topo-bathymetric surface models based on geospatial data recorded by UAV and USV vehicles

Lewicka Oktawia

Metrology and Measurement Systems

2023

Vol. 30, nr 3

461--480

Geospatial data obtained using Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs) are increasingly used to model the terrain in the coastal zone, in particular in shallow waterbodies (with a depth of up to 1 m). In order to generate a terrain relief, it is important to choose a method for modelling that will allow it to be accurately projected. Therefore, the aim of this article is to present a method for accuracy assessment of topo-bathymetric surface models based on geospatial data recorded by UAV and USV vehicles. Bathymetric and photogrammetric measurements were carried out on the waterbody adjacent to the public beach in Gdynia (Poland) in 2022 using a DJI Phantom 4 RTK UAV and an AutoDron USV. The geospatial data integration process was performed in the Surfer software. As a result, Digital Terrain Models (DTMs) in the coastal zone were developed using the following terrain modelling methods: Inverse Distance to a Power (IDP), Inverse Distance Weighted (IDW), kriging, the Modified Shepard’s Method (MSM) and Natural Neighbour Interpolation (NNI). The conducted study does not clearly indicate any of the methods, as the selection of the method is also affected by the visualization of the generated model. However, having compared the accuracy measures of the charts and models obtained, it was concluded that for this type of data, the kriging (linear model) method was the best. Very good results were also obtained for the NNI method. The lowest value of the Root Mean Square Error (RMSE) (0.030 m) and the lowest value of the Mean Absolute Error (MAE) (0.011 m) were noted for the GRID model interpolated with the kriging (linear model) method. Moreover, the NNI and kriging (linear model) methods obtained the highest coefficient of determination value (0.999). The NNI method has the lowest value of the R68 measure (0.009 m), while the lowest value of the R95 measure (0.033 m) was noted for the kriging (linear model) method.

Error mitigation algorithm based on bidirectional fitting method for collision avoidance of Unmanned Surface Vehicle

Song L., Chen Z., Mao Y., Dong Z., Xiang Z.

Polish Maritime Research

2018

nr 4

13--20

Radars and sensors are essential devices for an Unmanned Surface Vehicle (USV) to detect obstacles. Their precision has improved significantly in recent years with relatively accurate capability to locate obstacles. However, small detection errors in the estimation and prediction of trajectories of obstacles may cause serious problems in accuracy, thereby damaging the judgment of USV and affecting the effectiveness of collision avoidance. In this study, the effect of radar errors on the prediction accuracy of obstacle position is studied on the basis of the autoregressive prediction model. The cause of radar error is also analyzed. Subsequently, a bidirectional adaptive filtering algorithm based on polynomial fitting and particle swarm optimization is proposed to eliminate the observed errors in vertical and abscissa coordinates. Then, simulations of obstacle tracking and prediction are carried out, and the results show the validity of the algorithm. Finally, the method is used to simulate the collision avoidance of USV, and the results show the validity and reliability of the algorithm.