• Journal of Aerospace System Engineering
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• v.16 no.6
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• pp.1-7
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• 2022

A simulated radar terrain scan data generation method is employed for terrain following. This method scans the discrete terrain by sequentially radiating beams from the radar to the desired scan area with the same azimuth but varying elevation angles. The terrain data collected from the beam is integrated to generate the simulated radar terrain scan data, which comprises radar-detected points. However, these points can be located far from the beam centerline when the radar is far from them due to beam divergence. This paper proposes a geometry-based terrain scan data generation method for analysing simulated radar terrain scan data. The method involves detecting geometric points along the beam centerline, which forms the geometry-based terrain scan data. The analysis of the simulated radar terrain scan data utilising this method confirms that the beam width effects are accounted for in the results.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1445-1456
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• 2018

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.