A novel approach for a double-check of passable vegetation detection in autonomous ground vehicles

Nguyen, Duong-Van; Kuhnert, Lars; Thamke, Stefan; Schlemper, Jens; Kuhnert, Klaus-Dieter

doi:10.1109/itsc.2012.6338752

Cited by 20 publications

(10 citation statements)

References 31 publications

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“…Nguyen et al use a sliding cube across the 3D-point cloud in space to capture the local point statistics features. They segment the 3D-point cloud into three classes: surfaces, linear structures and porous volumes (foliage grass, tree canopy) [6], [7]. It can be argued that using only 3D-data, i.e.…”

Section: Related Workmentioning

confidence: 99%

“…For this, in [6], they use a coarse calibration method. This approach is time consuming and it should be used only when time is not a critical factor [7]. Liu et al, in a similar way, use a combination of 2D and 3D features.…”

Section: Related Workmentioning

confidence: 99%

“…Taking that into consideration and wanting to avoid time consumption needed for calibration and LiDAR scanning Nguyen et al developed a modified vegetation index MNDVI [7] which they use as a feature for vegetation detection. In [5] a combination of vegetation indices with color and texture features is used for vegetation detection.…”

Section: Related Workmentioning

confidence: 99%

“…Autonomous vehicles are increasingly used for offroad navigation where vegetation is present in larger or smaller quantities and it is necessary to decide if the vehicle can drive over, or avoid detected vegetation [3]- [7]. Not all vegetation and vegetation-like obstacles are static and need to be avoided (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Motion estimation aided detection of roadside vegetation

Harbas

Subašić

2014

2014 7th International Congress on Image and Signal Processing

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In this paper we present a method for roadside vegetation detection from video obtained from a moving vehicle with intended use in road infrastructure maintenance and traffic safety. While many published methods are using Near Infrared images which are suitable for vegetation detection, our method uses image features from the visible spectrum allowing the use of a common color camera. The presented detection method uses a set of carefully selected color and texture features. Texture features are based on two-dimensional Continuous Wavelet Transform with oriented wavelets. As selected features can vary with the distance from the camera, we are limiting detection to the regions near to the camera. We used an optical flow algorithm as an approximate estimator of the distance. The classification into vegetation and non-vegetation regions was done using nonlinear SVM. For training and testing purposes we recorded our own video database which contains roadside vegetation in various conditions. We are presenting promising experimental results, comparison with an alternative approach and a discussion of specific problems experienced or expected in real-world application of the method.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Motion estimation aided detection of roadside vegetation

Harbas

Subašić

2014

2014 7th International Congress on Image and Signal Processing

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“…Hence, it was necessary to detect vegetation and decide if the vehicle can drive over that obstacle or should it be avoided. Increased use of autonomous vehicles for off-road navigation is the main reason for new research efforts in detection and classification of vegetation [1], [3], [6], [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Detection of roadside vegetation using features from the visible spectrum

Harbas

Subašić

2014

2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

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Detection of vegetation in images is a common procedure in remote sensing and is commonly applied to satellite and aerial images. Recently it has been applied to images recorded from within ground vehicles for autonomous navigation in outdoor environments. In this paper we present a method for roadside vegetation detection intended for traffic safety and infrastructure maintenance. While many published methods for vegetation detection are using Near Infrared images which are particularly suitable for vegetation detection, our method uses image features from the visible spectrum allowing the use of common onboard color cameras. Our feature set consists of color features and texture features. One of our specific goals was to identify a useful texture feature set for the problem of vegetation detection. Based on the feature set, the detection is implemented using a Support Vector Machine algorithm. For training and testing purposes we recorded our own image database consisting of different images containing roadside vegetation in various conditions. We are presenting promising experimental results and a discussion of specific problems experienced or expected in real-world application of the method.

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Normal Distributions Transform Traversability Maps: LIDAR‐Only Approach for Traversability Mapping in Outdoor Environments

Ahtiainen

Stoyanov

Saarinen³

2016

Journal of Field Robotics

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Safe and reliable autonomous navigation in unstructured environments remains a challenge for field robots. In particular, operating on vegetated terrain is problematic, because simple purely geometric traversability analysis methods typically classify dense foliage as nontraversable. As traversing through vegetated terrain is often possible and even preferable in some cases (e.g., to avoid executing longer paths), more complex multimodal traversability analysis methods are necessary. In this article, we propose a three‐dimensional (3D) traversability mapping algorithm for outdoor environments, able to classify sparsely vegetated areas as traversable, without compromising accuracy on other terrain types. The proposed normal distributions transform traversability mapping (NDT‐TM) representation exploits 3D LIDAR sensor data to incrementally expand normal distributions transform occupancy (NDT‐OM) maps. In addition to geometrical information, we propose to augment the NDT‐OM representation with statistical data of the permeability and reflectivity of each cell. Using these additional features, we train a support‐vector machine classifier to discriminate between traversable and nondrivable areas of the NDT‐TM maps. We evaluate classifier performance on a set of challenging outdoor environments and note improvements over previous purely geometrical traversability analysis approaches.

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A novel approach for a double-check of passable vegetation detection in autonomous ground vehicles

Cited by 20 publications

References 31 publications

Motion estimation aided detection of roadside vegetation

Motion estimation aided detection of roadside vegetation

Detection of roadside vegetation using features from the visible spectrum

Normal Distributions Transform Traversability Maps: LIDAR‐Only Approach for Traversability Mapping in Outdoor Environments

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