A combination of vision- and vibration-based terrain classification

Weiß, Christian; Tamimi, Hashem; Zell, Andreas

doi:10.1109/iros.2008.4650678

Cited by 46 publications

(40 citation statements)

References 14 publications

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“…Note that the evaluation above excludes the earlier studies for outdoor terrains [1,2,11,17] since the surface recognition must distinguish more subtle details in the body oscillations on domestic floorings (see in Chapter I) with less surface irregularities. [6,12,15] were also omitted by the reason of the missing cross-validation step.…”

Section: A Model Accuracy Estimationmentioning

confidence: 99%

“…Learning visual cues in a house can enhance a vibration model, but creating a generic texture or color based classifier for all kinds of carpets, tiles and other floorings is an overwhelming task. By these reasons, the terrains were detected with higher accuracies by fused modalities outside compared to the indoor floorings [5] and the vision models suit better in natural environments [4,17]. These experimental conditions are examined in this paper.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…Various locomotion options and sensor combinations have been examined in the past works: wheeled [11,12,16,17], quadruped [3,4,8,9,10,13], six-legged robots [15]. Although some studies analyzed a few sensors [9,11] or the body oscillations [18], this paper does not go into this direction, but the focus is on the classifier model building and the evaluation to provide a better interpretation of the past results from machine learning point of view.…”

Section: Introduction and Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Rigidity-Based Surface Recognition for a Domestic Legged Robot

Kertész¹

2016

IEEE Robot. Autom. Lett.

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Abstract-Although the infrared (IR) range and motor force sensors have been rarely applied to the surface recognition of mobile robots, they are fused in this paper with accelerometer and ground contact force sensors to distinguish six indoor surface types. Their sensor values are affected by the crawling gait period, therefore, certain components of the fast Fourier transform over these data are included in the feature vectors as well as remarkable discriminative power is observed for the same scalar statistics of different sensing modalities. The machine learning aspects are analyzed with random forests (RF) because of their stable performance and some inherent, beneficial properties for the model development process. The robustness is evaluated with unseen data after the model accuracy is estimated with cross-validation (CV), and regardless whether a Sony ERS-7 walks barefoot or wears socks, the forests achieve 94% accuracy. This result outperforms the state of the art techniques for indoor surfaces in the literature and the classification execution is real-time on the robot. The above mentioned model development process with RF is documented to create new models for other robots more quickly and efficiently.

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Section: A Model Accuracy Estimationmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

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Rigidity-Based Surface Recognition for a Domestic Legged Robot

Kertész¹

2016

IEEE Robot. Autom. Lett.

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“…Further to this, Weiss et al tested the same method using a RWI ATRV-Jr robot and demonstrated that the SVM-based method outperformed other methods, including Brooks and Iagnemma's method, PNN, kNN, Naïve Bayes, and J4.8 decision tree [2]. In addition, a combination of vision-based and vibration-based methods was reported to significantly improve the classification rates when compared to single sensor-based prediction performance [16]. Finally, Brooks and Iagnemma introduced a self-supervised method suitable for environments with unexpected appearance [5].…”

Section: Introductionmentioning

confidence: 99%

A New Terrain Classification Framework Using Proprioceptive Sensors for Mobile Robots

Zhao

Dong

2017

Mathematical Problems in Engineering

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Mobile robots that operate in real-world environments interact with the surroundings to generate complex acoustics and vibration signals, which carry rich information about the terrain. This paper presents a new terrain classification framework that utilizes both acoustics and vibration signals resulting from the robot-terrain interaction. As an alternative to handcrafted domain-specific feature extraction, a two-stage feature selection method combining ReliefF and mRMR algorithms was developed to select optimal feature subsets that carry more discriminative information. As different data sources can provide complementary information, a multiclassifier combination method was proposed by considering a priori knowledge and fusing predictions from five data sources: one acoustic data source and four vibration data sources. In this study, four conceptually different classifiers were employed to perform the classification, each with a different number of optimal features. Signals were collected using a tracked robot moving at three different speeds on six different terrains. The new framework successfully improved classification performance of different classifiers using the newly developed optimal feature subsets. The greater improvement was observed for robot traversing at lower speeds.

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“…Although this approach has proven to be successful and robust to terrain and illumination changes during the Darpa Grand Challenge 2005, their proposal does not specify terrain types and is mainly tailored for desert terrain. Vibration and vision sensing are combined in [9] and [10]. The system in [10] provides a more accurate prediction than using vibration measurements alone.…”

Section: Introductionmentioning

confidence: 99%

Online learning terrain classification for adaptive velocity control

Mou

Kleiner

2010

2010 IEEE Safety Security and Rescue Robotics

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-Safe teleoperation during critical missions, such as urban search and rescue and bomb disposal, requires careful velocity control when different types of terrain are found in the scenario. This can particularly be challenging when mission time is limited and the operator's field of view affected. This paper presents a method for online adapting robot velocities according to the terrain classification from vision and laser readings. The classifier adapts itself to illumination variations, and can be improved online given feedback from the operator.

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A combination of vision- and vibration-based terrain classification

Cited by 46 publications

References 14 publications

Rigidity-Based Surface Recognition for a Domestic Legged Robot

Rigidity-Based Surface Recognition for a Domestic Legged Robot

A New Terrain Classification Framework Using Proprioceptive Sensors for Mobile Robots

Online learning terrain classification for adaptive velocity control

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