Generic vision based algorithm for driving space detection in diverse indoor and outdoor environments

Rasheed, Umer; Ahmed, Mariam; Ali, S.; Afridi, J. S.; Kunwar, Faraz

doi:10.1109/icma.2010.5588962

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…Some of the methods involve examining the image histogram, coupled with feature detection [2] or Canny and Hough transforms [3]. Progress has also been made using image segmentation techniques and logical operations [4].…”

Section: Introductionmentioning

confidence: 99%

Determining navigability of terrain using point cloud data

Cockrell

Lee

Newman

2013

2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR)

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This paper presents an algorithm to identify features of the navigation surface in front of a wheeled robot. Recent advances in mobile robotics have brought about the development of smart wheelchairs to assist disabled people, allowing them to be more independent. These robots have a human occupant and operate in real environments where they must be able to detect hazards like holes, stairs, or obstacles. Furthermore, to ensure safe navigation, wheelchairs often need to locate and navigate on ramps. The algorithm is implemented on data from a Kinect and can effectively identify these features, increasing occupant safety and allowing for a smoother ride.

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Section: Introductionmentioning

confidence: 99%

Determining navigability of terrain using point cloud data

Cockrell

Lee

Newman

2013

2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR)

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“…Therefore, more and more research is focusing on line extraction from laser scans [5]. A lot of research has been done about the point-distance-based (PDBS) method [6,7], the Kalman filtering-based (KFBS) method [8], the image-based method [9], and the Bayesian method [10]. Among these methods, the PDBS method has been widely used, such as in adaptive segmentation [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Two-dimensional laser feature extraction based on improved successive edge following

Zhang

Wang

et al. 2015

Appl. Opt.

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Successive edge following (SEF) has been widely used to describe the environmental characteristics based on a two-dimensional (2D) laser range finder due to its simplicity. However, the segmentation accuracy of the regular SEF for different distances is very low. And besides that, the regular SEF sometimes fails to characterize the corner features in the continuous segmentation. To solve these problems, we propose an improved SEF approach, which combines 2D polar radius-arc, adaptive threshold in a region to divide different radius data into groups more reasonably. Region growing is also discussed to localize the corner based on variance of segmentation and its fitting. Finally, all features described by line segments are used to build the map of the environment. As compared to the regular SEF, our approach is information-lossless. Our experimental results show that the segmentation accuracy is improved, while the segmentation number is more realistic and the feature detection accuracy is increased, as compared with the regular SEF.

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“…Common texture based road segmentation techniques are presented in [1]- [4]. Whereas improved approaches for road detection in structured and unstructured environments are given by Thorpe et al [5], [6].…”

Section: Introductionmentioning

confidence: 99%

A self-organizing neural scheme for road detection in varied environments

Malik

Ahmed

Kunwar

2011

The 2011 International Joint Conference on Neural Networks

Self Cite

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Detection of a drivable space is a key step in the autonomous control of a vehicle. In this paper we propose an adaptive vision based algorithm for road detection in diverse outdoor conditions. Our novel approach employs feature based classification and uses the Kohonen Self-Organizing Map (SOM) for the purpose of road detection. The robustness of the algorithm lies in the unique ability of SOM to organize information while learning diverse inputs. Features used for the training and testing of SOM are identified. The proposed method is capable of working with structured as well as unstructured roads and noisy environments that may be encountered by an intelligent vehicle. The proposed technique is extensively compared with the k-Nearest Neighbor (KNN) algorithm. Results show that SOM outperforms KNN in classification consistency and is independent to the lighting conditions while taking comparable classification time which shows that the network can also be used as an online learning architecture.

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Generic vision based algorithm for driving space detection in diverse indoor and outdoor environments

Cited by 3 publications

References 12 publications

Determining navigability of terrain using point cloud data

Determining navigability of terrain using point cloud data

Two-dimensional laser feature extraction based on improved successive edge following

A self-organizing neural scheme for road detection in varied environments

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