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


Wei Song, Shuanghui Zou, Yifei Tian, Su Sun, Simon Fong, Kyungeun Cho, Lvyang Qiu, Journal of Information Processing Systems Vol. 14, No. 6, pp. 1445-1456, Dec. 2018  

https://doi.org/10.3745/JIPS.02.0099
Keywords: Driving Awareness, Environment Perception, Unmanned Ground Vehicle, 3D Reconstruction
Fulltext:

Abstract

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.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from November 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.




Cite this article
[APA Style]
Song, W., Zou, S., Tian, Y., Sun, S., Fong, S., Cho, K., & Qiu, L. (2018). A CPU-GPU Hybrid System of Environment Perception and 3D Terrain Reconstruction for Unmanned Ground Vehicle. Journal of Information Processing Systems, 14(6), 1445-1456. DOI: 10.3745/JIPS.02.0099.

[IEEE Style]
W. Song, S. Zou, Y. Tian, S. Sun, S. Fong, K. Cho, L. Qiu, "A CPU-GPU Hybrid System of Environment Perception and 3D Terrain Reconstruction for Unmanned Ground Vehicle," Journal of Information Processing Systems, vol. 14, no. 6, pp. 1445-1456, 2018. DOI: 10.3745/JIPS.02.0099.

[ACM Style]
Wei Song, Shuanghui Zou, Yifei Tian, Su Sun, Simon Fong, Kyungeun Cho, and Lvyang Qiu. 2018. A CPU-GPU Hybrid System of Environment Perception and 3D Terrain Reconstruction for Unmanned Ground Vehicle. Journal of Information Processing Systems, 14, 6, (2018), 1445-1456. DOI: 10.3745/JIPS.02.0099.