Vibration-based Terrain Analysis for Mobile Robots

Brooks, Christopher; Iagnemma, Karl; Dubowsky, Steven

doi:10.1109/robot.2005.1570638

Cited by 54 publications

(36 citation statements)

References 13 publications

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“…Note that in our training setup, the slip measurements come from some unknown nonlinear functions ( Figure 2) and could not be simply clustered into well discriminable classes, as previously done for characterizing terrains from mechanical vibration signatures [5], [8], or for learning terrain traversability in self-supervised learning [6], [11], [14], [17]. So, using these slip measurements as supervision is not a trivial extension of supervised learning.…”

Section: Tha34mentioning

confidence: 99%

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Learning slip behavior using automatic mechanical supervision

Angelova

Matthies

Helmick

et al. 2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

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Abstract-We address the problem of learning terrain traversability properties from visual input, using automatic mechanical supervision collected from sensors onboard an autonomous vehicle. We present a novel probabilistic framework in which the visual information and the mechanical supervision interact to learn particular terrain types and their properties.The proposed method is applied to learning of rover slippage from visual information in a completely automatic fashion. Our experiments show that using mechanical measurements as automatic supervision significantly improves the visual-based classification alone and approaches the results of learning with manual supervision. This work will enable the rover to drive safely on slopes, learning autonomously about different terrains and their slip characteristics.

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Section: Tha34mentioning

confidence: 99%

“…Although mechanical sensor measurements have been used to characterize terrain [5], [8], [20], [26], they have not been used to close the loop in a fully automatic vision-based learning framework and no principled approach for learning using automatic mechanical supervision has been considered.…”

Section: Introductionmentioning

confidence: 99%

Learning slip behavior using automatic mechanical supervision

Angelova

Matthies

Helmick

et al. 2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

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“…Terrain types have also been classified using vibration sensors on a robot [13,14,15,16]. In these approaches, the robot traverses the terrain and the induced vibration is measured using accelerometers.…”

Section: Related Workmentioning

confidence: 99%

Identifying vegetation from laser data in structured outdoor environments

Wurm¹,

Kretzschmar²,

Kümmerle³

et al. 2014

Robotics and Autonomous Systems

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The ability to reliably detect vegetation is an important requirement for outdoor navigation with mobile robots as it enables the robot to navigate more efficiently and safely. In this paper, we present an approach to detect flat vegetation, such as grass, which cannot be identified using range measurements. This type of vegetation is typically found in structured outdoor environments such as parks or campus sites. Our approach classifies the terrain in the vicinity of the robot based on laser scans and makes use of the fact that plants exhibit specific reflection properties. It uses a support vector machine to learn a classifier for distinguishing vegetation from streets based on laser reflectivity, measured distance, and the incidence angle. In addition, it employs a vibration-based classifier to acquire training data in a self-supervised way and thus reduces manual work. Our approach has been evaluated extensively in real world experiments using several mobile robots. We furthermore evaluated it with different types of sensors and in the context of mapping, autonomous navigation, and exploration experiments. In addition, we compared it to an approach based on linear discriminant analysis. In our real world experiments, our approach yields a classification accuracy close to 100%.

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“…The proprioceptive data, acquired by an IMU, results in a vibration-based terrain characterization similar to [3], but with a single sensor measuring the rover structural vibrations. The exteroceptive data acquired by laser range sensors then leads to a classification based on 3D point clouds.…”

Section: Approach Overviewmentioning

confidence: 99%

“…In the literature, the two fields are clearly separated most of the time [1,2]. The first one aims at associating terrain with well-defined categories [3,4] whereas the terrain characterization tends to determine the driving performance corresponding to a terrain [5,6,7].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Boosting Based Terrain Classification Using Proprioceptive and Exteroceptive Data

Krebs¹,

Pradalier²,

Siegwart³

2009

Experimental Robotics

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Summary. The terrain classification is a very important subject to the all-terrain robotics community. The knowledge of the type of terrain allows a rover to deal with its environment more efficiently. The work presented in this paper shows that it is possible to differentiate terrains based on their aspects, using exteroceptive sensors, as well as based on their influence on the rover's behavior, using proprioceptive sensors. Using a boosting method (AdaBoost), these two sets of classifiers are trained and applied independently. The resulting dual algorithm identifies offline the nature of the terrain on which the vehicle is virtually driving and classifies it according to categories previously labeled, such as sand or grass. Due to the good results obtained for the classification based solely on each type of sensor, this paper concludes that the correlation between data from proprioceptive and exteroceptive sensors could be used for further applications. This paper is a summarized version of the one presented at the ISER conference.

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Vibration-based Terrain Analysis for Mobile Robots

Cited by 54 publications

References 13 publications

Learning slip behavior using automatic mechanical supervision

Learning slip behavior using automatic mechanical supervision

Identifying vegetation from laser data in structured outdoor environments

Comparison of Boosting Based Terrain Classification Using Proprioceptive and Exteroceptive Data

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