Region Classification for Robust Floor Detection in Indoor Environments

Fazl-Ersi, Ehsan; Tsotsos, John K.

doi:10.1007/978-3-642-02611-9_71

Cited by 23 publications

(11 citation statements)

References 7 publications

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“…The flow chart of the appearance based obstacle detection systems is illustrated in Figure 2. The input image is first convolved with a smoothing filter to reduce the noise effects, and then smoothed image is converted to HIS, HSV or any related colour space with respect to the developed algorithm (Fazl-Ersi & Tsotsos, 2009). A reference area is obtained from this image which might be any shape of geometry such as trapezoidal, triangle or square, and histogram values of this reference area are generated (Saitoh et al, 2009).…”

Section: Appearance-based Methodsmentioning

confidence: 99%

Vision Based Obstacle Avoidance Techniques

Güzel¹,

Bicker²

2011

Recent Advances in Mobile Robotics

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Section: Appearance-based Methodsmentioning

confidence: 99%

Vision Based Obstacle Avoidance Techniques

Güzel¹,

Bicker²

2011

Recent Advances in Mobile Robotics

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“…To begin with, choosing appropriate sensors is a not a trivial task and tends to result in a trade-off between many issues, such as: cost, precision, range, robustness, sensitivity, complexity of post-processing and so on. Furthermore, no single sensor by it- The obstacle-detection algorithm is based upon a computer-vision approach prosed in [13], but adapted for monocular vision. The floor is deemed to be the largest region that touches the base of the image, yet does not cross the horizon.…”

Section: A Perceptionmentioning

confidence: 99%

“…The concept of the algorithm is to detect the floor region and label everything that does not fall into this region as an obstacle; we follow an approach similar to that proposed in [13], albeit with monocular vision, rather than using a stereo head.…”

Section: B Computer Vision-based Obstacle Detectionmentioning

confidence: 99%

Brain-Controlled Wheelchairs: A Robotic Architecture

Carlson

Millán

2013

IEEE Robot. Automat. Mag.

384

246

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Abstract-Independent mobility is core to being able to perform activities of daily living by oneself. However, powered wheelchairs are not an option for a large number of people who are unable to use conventional interfaces, due to severe motor-disabilities. For some of these poeple, non-invasive braincomputer interfaces (BCIs) offer a promising solution to this interaction problem and in this article we present a shared control architecture that couples the intelligence and desires of the user with the precision of a powered wheelchair. We show how four healthy subjects are able to master control of the wheelchair using an asynchronous motor-imagery based BCI protocol and how this results in a higher overall task performance, compared with alternative synchronous P300-based approaches.

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“…In the remainder of this section, we discuss publications that also apply automatic training. FazlErsi and Tsotsos use stereo data to extract dense information about floor and obstacles 33 . The authors proposed to classify regions of neighboring pixels with similar color information and also consider the distances from the ground plane to distinguish between floor and obstacles.…”

Section: General Obstacle Detection Techniques Based On Visual Informmentioning

confidence: 99%

Vision-Based Humanoid Navigation Using Self-Supervised Obstacle Detection

Maier

Stachniss

Bennewitz

2013

Int. J. Human. Robot.

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In this article, we present an efficient approach to obstacle detection for humanoid robots based on monocular images and sparse laser data. We particularly consider collision-free navigation with the Nao humanoid, which is the most popular small-size robot nowadays. Our approach first analyzes the scene around the robot by acquiring data from a laser range finder installed in the head. Then, it uses the knowledge about obstacles identified in the laser data to train visual classifiers based on color and texture information in a self-supervised way. While the robot is walking, it applies the learned classifiers to the camera images to decide which areas are traversable. As we show in the experiments, our technique allows for safe and efficient humanoid navigation in real-world environments, even in the case of robots equipped with low-end hardware such as the Nao, which has not been achieved before. Furthermore, we illustrate that our system is generally applicable and can also support the traversability estimation using other combinations of camera and depth data, e.g., from a Kinect-like sensor.

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Region Classification for Robust Floor Detection in Indoor Environments

Cited by 23 publications

References 7 publications

Vision Based Obstacle Avoidance Techniques

Vision Based Obstacle Avoidance Techniques

Brain-Controlled Wheelchairs: A Robotic Architecture

Vision-Based Humanoid Navigation Using Self-Supervised Obstacle Detection

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