Improving Robot Mobility by Combining Downward-Looking and Frontal Cameras

González, Roberto J.; Rituerto, Alejandro; Guerrero, J. J.

doi:10.3390/robotics5040025

Cited by 9 publications

(5 citation statements)

References 28 publications

(30 reference statements)

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“…A downward‐looking camera was employed in Refs. and for measuring the actual forward velocity of a tracked mobile robot. This strategy implemented a particular VO‐based algorithm (i.e., template matching).…”

Section: Literature Reviewmentioning

confidence: 99%

Slippage estimation and compensation for planetary exploration rovers. State of the art and future challenges

González

Iagnemma

2017

Journal of Field Robotics

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Future lunar/planetary exploration missions will demand mobile robots with the capability of reaching more challenging science targets and driving farther per day than the current Mars rovers. Among other improvements, reliable slippage estimation and compensation strategies will play a key role in enabling a safer and more efficient navigation. This paper reviews and discusses this body of research in the context of planetary exploration rovers. Previously published state‐of‐the‐art methods that have been validated through field testing are included as exemplary results. Limitations of the current techniques and recommendations for future developments and planetary missions close the survey.

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Section: Literature Reviewmentioning

confidence: 99%

Slippage estimation and compensation for planetary exploration rovers. State of the art and future challenges

González

Iagnemma

2017

Journal of Field Robotics

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“…gravel (ground and panoramic images), sand (ground images), grass (ground images), pavement (ground and panoramic images), and asphalt (ground and panoramic images) [16].…”

Section: Mobile Robot Fitorobotmentioning

confidence: 99%

“…These descriptors are based on a similar idea, that is, applying a bank of filters at various locations, scales and orientations to an image. The average of these filters then gives the image signature [16].…”

Section: Feature Selection For Image Classificationmentioning

confidence: 99%

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DeepTerramechanics: Terrain Classification and Slip Estimation for Ground Robots via Deep Learning

Gonzalez,

Iagnemma

2018

Preprint

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Terramechanics plays a critical role in the areas of ground vehicles and ground mobile robots since understanding and estimating the variables influencing the vehicle-terrain interaction may mean the success or the failure of an entire mission. This research applies state-of-the-art algorithms in deep learning to two key problems: estimating wheel slip and classifying the terrain being traversed by a ground robot. Three data sets collected by ground robotic platforms (MIT single-wheel testbed, MSL Curiosity rover, and tracked robot Fitorobot) are employed in order to compare the performance of traditional machine learning methods (i.e. Support Vector Machine (SVM) and Multilayer Perceptron (MLP)) against Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs). This work also shows the impact that certain tuning parameters and the network architecture (MLP, DNN and CNN) play on the performance of those methods. This paper also contributes a deep discussion with the lessons learned in the implementation of DNNs and CNNs and how these methods can be extended to solve other problems.

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“…Comparative study of different features (including Color and Edge Directivity Descriptor (CEDD), Fuzzy Color and Texture Histogram (FCTH) and Joint Composite Descriptor (JCD)) and classifiers (including Extreme Learning Machine (ELM), Support Vector Machine (SVM) and Neural Network (NN)) applying it to visual terrain classification is presented in [17].Experiment results demonstrate that the combination of JCD and ELM has the highest generalisation performance. In [18], downward and forward-looking cameras are both employed to recognise the terrain being traversed and that to be traversed, respectively. The downward-looking terrain images are used to improve the prediction of the coming terrain.…”

Section: Introductionmentioning

confidence: 99%

Laplacian Support Vector Machine for Vibration-Based Robotic Terrain Classification

Shi

et al. 2020

Electronics

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The achievement of robot autonomy has environmental perception as a prerequisite. The hazards rendered from uneven, soft and slippery terrains, which are generally named non-geometric hazards, are another potential threat reducing the traversing efficient, and therefore receiving more and more attention from the robotics community. In the paper, the vibration-based terrain classification (VTC) is investigated by taking a very practical issue, i.e., lack of labels, into consideration. According to the intrinsic temporal correlation existing in the sampled terrain sequence, a modified Laplacian SVM is proposed to utilise the unlabelled data to improve the classification performance. To the best of our knowledge, this is the first paper studying semi-supervised learning problem in robotic terrain classification. The experiment demonstrates that: (1) supervised learning (SVM) achieves a relatively low classification accuracy if given insufficient labels; (2) feature-space homogeneity based semi-supervised learning (traditional Laplacian SVM) cannot improve supervised learning’s accuracy, and even makes it worse; (3) feature- and temporal-space based semi-supervised learning (modified Laplacian SVM), which is proposed in the paper, could increase the classification accuracy very significantly.

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Improving Robot Mobility by Combining Downward-Looking and Frontal Cameras

Cited by 9 publications

References 28 publications

Slippage estimation and compensation for planetary exploration rovers. State of the art and future challenges

Slippage estimation and compensation for planetary exploration rovers. State of the art and future challenges

DeepTerramechanics: Terrain Classification and Slip Estimation for Ground Robots via Deep Learning

Laplacian Support Vector Machine for Vibration-Based Robotic Terrain Classification

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