Self Navigation and Positioning Robot - SNAPBot

Das, Amrit; Giri, Rohan; Chatterjee, Ayon; Singh, Vanshika; Dhanalakshmi, Samiappan; Kumar, Rishabh

doi:10.1109/icict48043.2020.9112587

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…In the navigation based on multiple terrains, a robot needs to automatically segment different terrains, including cement ground, grass, and puddles, to know its current surroundings quickly, plan appropriate paths, or make reasonable decisions. [3][4][5] Yet, many challenges exist in terrain segmentation, as the outdoor environment is usually unstructured, uncertain, and sometimes interfered with by light, weather, pedestrians, vehicles, and other disturbances. 6,7 Therefore, it is crucial to improve the accuracy and real-time performance of terrain segmentation.…”

Section: Introductionmentioning

confidence: 99%

A terrain segmentation method based on pyramid scene parsing-mobile network for outdoor robots

Zhang

Hong

Xiong

et al. 2021

International Journal of Advanced Robotic Systems

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Terrain segmentation is of great significance to robot navigation, cognition, and map building. However, the existing vision-based methods are challenging to meet the high-accuracy and real-time performance. A terrain segmentation method with a novel lightweight pyramid scene parsing mobile network is proposed for terrain segmentation in robot navigation. It combines the feature extraction structure of MobileNet and the encoding path of pyramid scene parsing network. The depthwise separable convolution, the spatial pyramid pooling, and the feature fusion are employed to reduce the onboard computing time of pyramid scene parsing mobile network. A unique data set called Hangzhou Dianzi University Terrain Dataset is constructed for terrain segmentation, which contains more than 4000 images from 10 different scenes. The data set was collected from a robot’s perspective to make it more suitable for robotic applications. Experimental results show that the proposed method has high-accuracy and real-time performance on the onboard computer. Moreover, its real-time performance is better than most state-of-the-art methods for terrain segmentation.

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