Colour based off-road environment and terrain type classification

Jansen, Peter; Mark, W. van der; Heuvel, J.C. van den; Groen, F.C.A.

doi:10.1109/itsc.2005.1520023

Cited by 31 publications

(23 citation statements)

References 6 publications

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“…from front mounted cameras, and applying image processing techniques (Jansen et al, 2005;Tang and Breckon, 2011). In their work, Tang and Breckon (2011) use color, texture and edge features from image sub-regions as inputs into a neural network, and using colour analysis, Jansen et al (2005) identify the terrain type. Such systems are limited because they rely on non-standard sensors, generally need greater computational processing and are severely affected by poor lighting conditions, such as night-time driving.…”

Section: Related Workmentioning

confidence: 99%

Data Mining for Vehicle Telemetry

Taylor

Griffiths

Bhalerao

et al. 2016

Applied Artificial Intelligence

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This paper presents a data mining methodology for driving condition monitoring via CAN-bus data that is based on the general data mining process. The approach is applicable to many driving condition problems and the example of which is demonstrated to successfully ascertain road type from CAN-bus data, and it is shown that the classification correlates well with input signals such as vehicle speed, steering wheel angle, and suspension height.

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Section: Related Workmentioning

confidence: 99%

Data Mining for Vehicle Telemetry

Taylor

Griffiths

Bhalerao

et al. 2016

Applied Artificial Intelligence

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“…Other combinations include brightness-normalized color with a mixture of Gaussians in an ML framework (Manduchi et al, 2005); hue-saturation-value (HSV) color and wavelet-based texture with an SVM classifier (Halatci, Brooks, & Iagnemma, 2007); and red-green-blue (RGB) color with a mixture of Gaussians in an ML framework (Jansen, van der Mark, van den Heuvel, & Groen, 2005).…”

Section: Color-based Terrain Classificationmentioning

confidence: 99%

Image‐based path planning for outdoor mobile robots

Huang

Ollis

Happold

et al. 2009

Journal of Field Robotics

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Mobile robots operating in natural terrain need some sort of long-range perception in order to navigate efficiently. Whereas LADAR is a commonly used sensor on such systems, providing range data out to 25 m and beyond, we have instead focused on what information can be extracted from vision. Our robot has only two stereo camera pairs for terrain sensing; they provide reliable stereo data up to 5 m away, but this is not enough to prevent myopic behavior. To overcome this problem, we have developed a novel approach to navigation using monocular imagery by planning a path in the image space. We take a monocular image and apply a learned color-to-cost mapping to transform the raw image into a cost image. Then, after a pseudo-configuration-space transform, we search for a pixel-topixel path from a point in front of the robot to the projected goal point in the cost image. Our implementation has been shown to react to obstacles at a range of 93 m, far beyond Supporting videos (see Section 4) are available in the online issue at www.interscience.wiley.com.

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“…Road segmentation is the focus of many research works (Lombardi et al, 2005;Jansen et al, 2005;Guzman & Parra, 2007;Alvarez et al, 2008). In our study, the sought segmentation should meet two requirements: (i) it should be as fast as possible, and (ii) it should be as generic as possible (both urban roads and highways).…”

Section: Road Segmentationmentioning

confidence: 99%