Abstract
Remote vehicle operator must quickly decide on the motion and path. Thus, rapid and intuitive feedback of the real environment is vital for effective control. This paper presents a real-time traversable ground surface segmentation and intuitive representation system for remote operation of mobile robot. Firstly, a terrain model using voxel-based flag map is proposed for incrementally registering large-scale point clouds in real time. Subsequently, a ground segmentation method with Gibbs-Markov random field (Gibbs-MRF) model is applied to detect ground data in the reconstructed terrain. Finally, we generate a texture mesh for ground surface representation by mapping the triangles in the terrain mesh onto the captured video images. To speed up the computation, we program a graphics processing unit (GPU) to implement the proposed system for large-scale datasets in parallel. Our proposed methods were tested in an outdoor environment. The results show that ground data is segmented effectively and the ground surface is represented intuitively.
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