A CPU-GPU Hybrid System of Environment Perception and 3D Terrain Reconstruction for Unmanned Ground Vehicle

• Published in: Journal of information processing systems 2018, 14(6), 54, pp.1445-1456
• Main Authors: Wei Song, Shuanghui Zou, Yifei Tian, Su Sun, Simon Fong, 조경은, Lvyang Qiu
• Format: Article
• Language: English
• Subjects: 컴퓨터학
• ISSN: 1976-913X; 2092-805X
• DOI: 10.3745/JIPS.02.0099

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmannedground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surroundingterrain reconstruction crucially influences decision making in UGVs. To increase the processing speed ofenvironment information analysis, we develop a CPU-GPU hybrid system of automatic environmentperception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists ofthree 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-processingfunction registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motioninformation into a global terrain model after filtering redundant and noise data according to the redundancyremoval principle. In the environment perception module, the registered discrete points are clustered intoground surface and individual objects by using a ground segmentation method and a connected componentlabeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversedand obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibratesthe projection matrix between the mounted LiDAR and cameras to map the local point clouds onto thecaptured video images. Texture meshes and color particle models are used to reconstruct the ground surfaceand objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallelcomputation method to implement the applied computer graphics and image processing algorithms in parallel. KCI Citation Count: 1