Surface Type Classification for Autonomous Robots Using Temporal, Statistical and Spectral Feature Extraction and Selection

Hasan, Md. Al Mehedi; Abir, Fuad Al; Shin, Jungpil

doi:10.1109/mcsoc51149.2021.00029

Cited by 1 publication

(2 citation statements)

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“…Gyroscopes, which measure angular velocity around one or more axes, are widely used in pattern recognition applications, such as human movement or activity recognition [32,33]. In terrain classification tasks, these sensors were mainly tested together with accelerometers [31,[34][35][36][37][38]. In a previous study, the authors of this paper showed that gyroscopes can provide significantly higher classification efficiencies than accelerometers using a frequency domain-based feature set [39].…”

Section: Introductionmentioning

confidence: 98%

“…In [36], more than 800 features were extracted from the inertial sensor signals for indoor terrain classification with a linear Bayes normal classifier. Hasan et al applied altogether 60 different temporal, statistical and spectral features using accelerometer and gyroscope data together to classify 9 indoor surface types [37]. In [38], the components of the amplitude spectrum computed for the six channels of the IMU sensor were used together as inputs of the ANN to classify terrains into five classes, i.e., indoor floor, asphalt, grass, soil, and loose gravel.…”

Section: Introductionmentioning

confidence: 99%

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Online Outdoor Terrain Classification Algorithm for Wheeled Mobile Robots Equipped with Inertial and Magnetic Sensors

et al. 2023

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Terrain classification provides valuable information for both control and navigation algorithms of wheeled mobile robots. In this paper, a novel online outdoor terrain classification algorithm is proposed for wheeled mobile robots. The algorithm is based on only time-domain features with both low computational and low memory requirements, which are extracted from the inertial and magnetic sensor signals. Multilayer perceptron (MLP) neural networks are applied as classifiers. The algorithm is tested on a measurement database collected using a prototype measurement system for various outdoor terrain types. Different datasets were constructed based on various setups of processing window sizes, used sensor types, and robot speeds. To examine the possibilities of the three applied sensor types in the application, the features extracted from the measurement data of the different sensors were tested alone, in pairs and fused together. The algorithm is suitable to operate online on the embedded system of the mobile robot. The achieved results show that using the applied time-domain feature set the highest classification efficiencies on unknown data can be above 98%. It is also shown that the gyroscope provides higher classification rates than the widely used accelerometer. The magnetic sensor alone cannot be effectively used but fusing the data of this sensor with the data of the inertial sensors can improve the performance.

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