An Efficient Deep Representation Based Framework for Large-Scale Terrain Classification

Yan, Yupeng; Rangarajan, Anand; Ranka, Sanjay

doi:10.1109/icpr.2018.8545021

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…Digital image-based methods mainly extract visual features from terrain images using local binary patterns (LBP), bag of visual words (BOVW) or speeded up robust features (SURF) [ 10 , 11 , 25 , 26 ]. Recently, convolutional neural networks (CNNs) have attracted more attention due to their excellent feature extracting capability, and thus applying to near-range and far-range terrain classification and planetary surface soil property analysis [ 27 , 28 ]. Digital cameras may not perform well in the dark.…”

Section: Related Workmentioning

confidence: 99%

Comparative Study of Different Methods in Vibration-Based Terrain Classification for Wheeled Robots with Shock Absorbers

Mei

Jin-yi

et al. 2019

Sensors

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Autonomous robots that operate in the field can enhance their security and efficiency by accurate terrain classification, which can be realized by means of robot-terrain interaction-generated vibration signals. In this paper, we explore the vibration-based terrain classification (VTC), in particular for a wheeled robot with shock absorbers. Because the vibration sensors are usually mounted on the main body of the robot, the vibration signals are dampened significantly, which results in the vibration signals collected on different terrains being more difficult to discriminate. Hence, the existing VTC methods applied to a robot with shock absorbers may degrade. The contributions are two-fold: (1) Several experiments are conducted to exhibit the performance of the existing feature-engineering and feature-learning classification methods; and (2) According to the long short-term memory (LSTM) network, we propose a one-dimensional convolutional LSTM (1DCL)-based VTC method to learn both spatial and temporal characteristics of the dampened vibration signals. The experiment results demonstrate that: (1) The feature-engineering methods, which are efficient in VTC of the robot without shock absorbers, are not so accurate in our project; meanwhile, the feature-learning methods are better choices; and (2) The 1DCL-based VTC method outperforms the conventional methods with an accuracy of 80.18%, which exceeds the second method (LSTM) by 8.23%.

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