Traversable Ground Surface Segmentation and Modeling for Real-Time Mobile Mapping

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.

3D Reconstruction Framework for Multiple Remote Robots on Cloud System

“…Several studies have been conducted on reconstruction in both indoor and outdoor environments [30][31][32][33][34][35][36]. In the indoor environment, Izadi et al [30] and Popescu and Lungu [31] employed a Microsoft Kinect sensor.…”

“…Khatamian and Arabnia [32] reviewed several 3D surface reconstruction methods from unoriented input point clouds and concluded that none of them are able to run in real-time. In [33][34][35][36], the authors used one 3D laser range scanner combined with cameras and an IMU-GPS sensor for the outdoor environment. Because the features of ground and non-ground data are different, they have to be segmented and reconstructed separately.…”

“…The method used to reconstruct the 3D terrain is based on the ideas presented in [35,36]. The architecture running in each reconstruction server is illustrated in Figure 2.…”

“…In this study, we employed the cloud model to create a three-dimensional (3D) reconstruction framework for multiple remote robots. The 3D reconstruction [30][31][32][33][34][35][36] has been widely studied as an approach to enhance the sense of reality and immersion for users. However, accomplishing this technique in real-time and with high quality is challenging, particularly with multiple remote robots.…”

“…Furthermore, the non-ground data are modeled using colored particles [35]. On the other hand, the ground data are reconstructed by meshing and texture mapping [36]. The results from all reconstruction servers are then sent to a visualization server to save in a database.…”