Terrain Classification and Classifier Fusion for Planetary Exploration Rovers

Halatci, Ibrahim; Brooks, Christopher; Iagnemma, Karl

doi:10.1109/aero.2007.352692

Cited by 61 publications

(35 citation statements)

References 25 publications

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“…Halatci et al [22] developed a terrain classifier with color, texture, and geometric features to identify rocky, sand, and mixed terrain in Mars rover imagery. Brooks and Iagnemma [7] expanded upon this classifier to instead predict coefficient of traction with a multi-class SVM.…”

Section: Related Workmentioning

confidence: 99%

Improving Slip Prediction on Mars Using Thermal Inertia Measurements

Cunningham

Nesnas

Whittaker

2017

Robotics: Science and Systems XIII

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Abstract-Rovers operating on Mars have been delayed, diverted, and trapped by loose granular materials. Vision-based mobility prediction approaches often fail because hazardous sand is difficult to distinguish from safe sand based on surface appearance alone. Unlike surface appearance, the thermal inertia of terrain is directly correlated to the same geophysical properties that control slip. This paper presents a quantitative analysis showing that considering thermal inertia improves rover slip prediction on Mars using in-situ data from the Curiosity rover. Thermal inertia is estimated for each slip measurement in sand using both on-board and orbital instruments. Slip models are learned using a mixture of experts approach where the experts are identified using thermal inertia. Two-expert models are compared to a single-expert, vision-only model to show that slip predictions are improved by separating high-slip, low thermal inertia sand from low-slip, high thermal inertia sand. These results support the hypothesis that the consideration of thermal inertia improves mobility estimates for rovers on Mars.

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Section: Related Workmentioning

confidence: 99%

Improving Slip Prediction on Mars Using Thermal Inertia Measurements

Cunningham

Nesnas

Whittaker

2017

Robotics: Science and Systems XIII

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“…Also wheeled robots can use properties of the contact with the ground to support classi ication procedure (e.g. vibrations which propagate through suspension structure [11]). …”

Section: Related Work and Research Contribu Onmentioning

confidence: 99%

Boosting Support Vector Machines for RGB-D Based Terrain Classification

Wietrzykowski¹,

Belter²

2014

JAMRIS

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“…The SG scheme and similar schemes have also been studied as a means of combining classifier predictions in other classification tasks [13], [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Methods for Topic Classification of Spoken Inquiries

Torres

Kawanami

Matsui

et al. 2013

Journal of Information Processing

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Abstract:In this work, we address the topic classification of spoken inquiries in Japanese that are received by a speech-oriented guidance system operating in a real environment. The classification of spoken inquiries is often hindered by automatic speech recognition (ASR) errors, the sparseness of features and the shortness of spontaneous speech utterances. Here, we compare the performances of a support vector machine (SVM) with a radial basis function (RBF) kernel, PrefixSpan boosting (pboost) and the maximum entropy (ME) method, which are supervised learning methods. We also combine their predictions using a stacked generalization (SG) scheme. We also perform an evaluation using words or characters as features for the classifiers. Using characters as features is possible in Japanese owing to the presence of kanji, ideograms originating from Chinese characters that represent not only sounds but also meanings. We performed analyses on the performance of the above methods and their combination in dealing with the indicated problems. Experimental results show an F-measure of 86.87% for the classification of ASR results from children's inquiries with an average performance improvement of 2.81% compared with the performance of individual classifiers, and an F-measure of 93.96% with an average improvement of 1.89% for adults' inquiries when using the SG scheme and character features.

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Terrain Classification and Classifier Fusion for Planetary Exploration Rovers

Cited by 61 publications

References 25 publications

Improving Slip Prediction on Mars Using Thermal Inertia Measurements

Improving Slip Prediction on Mars Using Thermal Inertia Measurements

Boosting Support Vector Machines for RGB-D Based Terrain Classification

Comparison of Methods for Topic Classification of Spoken Inquiries

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