Visual Terrain Classification Methods for Mobile Robots Using Hybrid Coding Architecture

Wu, Haiyuan; Zhang, Wenchang; Li, Boyang; Sun, Yucao; Duan, Deguang; Chen, Ping

doi:10.1109/icivc47709.2019.8981092

Cited by 5 publications

(5 citation statements)

References 20 publications

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“…This led to enhanced accuracy and gait speed in the robot's terrain recognition. Wu et al [ 193 ] combined the stacked denoising sparse automatic encoder and fisher vector techniques to achieve parameter self‐tuning through unsupervised machine learning. They conducted outdoor experiments on a quadruped robot with curved legs and obtained promising results in terrain recognition using datasets from various terrains.…”

Section: Autonomous Motionmentioning

confidence: 99%

A Review of Quadruped Robots: Structure, Control, and Autonomous Motion

Fan,

Pei,

Wang

et al. 2024

Advanced Intelligent Systems

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With their unique point‐contact ability with the ground and exceptional adaptability to complex terrains, quadruped robots have become a focal point in the fields of automation and robotic engineering. Significant research progress has been made in aspects such as structural design, motion planning, and balance control of these robots. However, a primary challenge in current research lies in further enhancing the dynamic performance, environmental adaptability, and payload capacity. In this article, research achievements in key technical areas of quadruped robots, encompassing structural design, gait planning, traditional control strategies, intelligent control strategies, and autonomous movement, are comprehensively discussed. The focus of the article is on analyzing trends in intelligence and technological innovation within the aforementioned areas, aiming to provide robust theoretical support and forward‐looking technical guidance for quadruped robots research. Additionally, it offers valuable references for scholars engaged in this field.

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Section: Autonomous Motionmentioning

confidence: 99%

A Review of Quadruped Robots: Structure, Control, and Autonomous Motion

Fan,

Pei,

Wang

et al. 2024

Advanced Intelligent Systems

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“…The terrain image dataset Terrain8 [ 27 ] was used to evaluate the effectiveness of our method for visual terrain classification. Those images were all earth terrain images.…”

Section: Experiments Verificationmentioning

confidence: 99%

“…Based on the above experiment results, the RF classifier was selected as terrain classifier. The proposed method was compared with deep filter banks (DFBs) [ 27 ], hierarchical coding vectors (HCVs) [ 28 ], Fisher vector (FV) [ 29 ], LBP. Table 9 shows the classification results for five classification methods.…”

Section: Experiments Verificationmentioning

confidence: 99%

Highly Accurate Visual Method of Mars Terrain Classification for Rovers Based on Novel Image Features

Gao

et al. 2022

Entropy

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It is important for Mars exploration rovers to achieve autonomous and safe mobility over rough terrain. Terrain classification can help rovers to select a safe terrain to traverse and avoid sinking and/or damaging the vehicle. Mars terrains are often classified using visual methods. However, the accuracy of terrain classification has been less than 90% in read operations. A high-accuracy vision-based method for Mars terrain classification is presented in this paper. By analyzing Mars terrain characteristics, novel image features, including multiscale gray gradient-grade features, multiscale edges strength-grade features, multiscale frequency-domain mean amplitude features, multiscale spectrum symmetry features, and multiscale spectrum amplitude-moment features, are proposed that are specifically targeted for terrain classification. Three classifiers, K-nearest neighbor (KNN), support vector machine (SVM), and random forests (RF), are adopted to classify the terrain using the proposed features. The Mars image dataset MSLNet that was collected by the Mars Science Laboratory (MSL, Curiosity) rover is used to conduct terrain classification experiments. The resolution of Mars images in the dataset is 256 × 256. Experimental results indicate that the RF classifies Mars terrain at the highest level of accuracy of 94.66%.

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“…Using classifiers to train different terrain features, and then matched with the collected environment image information to achieve the terrain environment recognition. 26,27 In this study, random forest classifier is investigated for classification. 28 Random forest is a classifier that uses multiple trees to train and predict samples.…”

Section: Classificationmentioning

confidence: 99%

Autonomous recognition technology of carrier robot on various terrain environment

Dong

Song²,

Luan³

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Field carrier robot can help people carry a certain amount of necessary materials in mountaineering, exploration and other fields, and even help field soldiers carry a certain amount of armaments. However, due to the complexity of the field environment, it is difficult for a robot with a single locomotion mechanism to complete such work. A carrier robot with leg/caterpillar/wheel is proposed. The robot can recognize different terrain environment, so as to select the appropriate locomotion mechanism, and can avoid obstacles and plan the path at the same time. We optimize the traditional ORB algorithm to extract the features of the environment image. The random forest classifier is used to classify the terrain, so as to start the corresponding locomotion mechanism. In addition, the extracted ORB feature points are also used for stereo matching to achieve the depth recovery for environmental information. Based on the analysis of two path planning algorithms of DWA and artificial potential field, an artificial potential field algorithm based on DWA is proposed for robot local path planning. In order to verify the effectiveness of these methods, experiments are carried out on the switching for three locomotion mechanisms and path planning in different practical environments. The results show that when the terrain changes, the robot can effectively switch the locomotion mechanism, and can run stably in different environments.

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Visual Terrain Classification Methods for Mobile Robots Using Hybrid Coding Architecture

Cited by 5 publications

References 20 publications

A Review of Quadruped Robots: Structure, Control, and Autonomous Motion

A Review of Quadruped Robots: Structure, Control, and Autonomous Motion

Highly Accurate Visual Method of Mars Terrain Classification for Rovers Based on Novel Image Features

Autonomous recognition technology of carrier robot on various terrain environment

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