Geographic information for vision-based road detection

Álvarez, Jose M.; Lumbreras, Felipe; Gevers, Theo; López, Antonio

doi:10.1109/ivs.2010.5548002

Cited by 14 publications

(10 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Alvares et al addressed this issue by employing digital street maps as arbitrary road shapes, which could be expressed in a map [10]. Although our method also uses digital street maps, significant differences exist between our method and the earlier study.…”

Section: Related Workmentioning

confidence: 99%

Road recognition from a single image using prior information

Irie

Tomono

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

In this study, we present a novel road recognition method using a single image for mobile robot navigation. Visionbased road recognition in outdoor environments remains a significant challenge. Our approach exploits digital street maps, the robot position, and prior knowledge of the environment. We segment an input image into superpixels, which are grouped into various object classes such as roadway, sidewalk, curb, and wall. We formulate the classification problem as an energy minimization problem and employ graph cuts to estimate the optimal object classes in the image. Although prior information assists recognition, erroneous information can lead to false recognition. Therefore, we incorporate localization into our recognition method to correct errors in robot position. The effectiveness of our method was verified through experiments using real-world urban datasets.

show abstract

Section: Related Workmentioning

confidence: 99%

Road recognition from a single image using prior information

Irie

Tomono

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

show abstract

“…Every way is made up of a list of nodes with a location and its relationship with the other nodes and ways. Thanks to the location and relationship between nodes, the shape of the current road and the surrounding streets may be estimated [31].…”

Section: Map-based Modelsmentioning

confidence: 99%

A Hybrid Vision-Map Method for Urban Road Detection

Fernández

Llorca

Sotelo

2017

Journal of Advanced Transportation

View full text Add to dashboard Cite

A hybrid vision-map system is presented to solve the road detection problem in urban scenarios. The standardized use of machine learning techniques in classification problems has been merged with digital navigation map information to increase system robustness. The objective of this paper is to create a new environment perception method to detect the road in urban environments, fusing stereo vision with digital maps by detecting road appearance and road limits such as lane markings or curbs. Deep learning approaches make the system hard-coupled to the training set. Even though our approach is based on machine learning techniques, the features are calculated from different sources (GPS, map, curbs, etc.), making our system less dependent on the training set.

show abstract

“…2(c)]. Rotation (γ, β, α) and translation (l x , l y , l z ) parameters can be set using calibration through registration [18] or empirical calibration [19]. The former consists of matching the projected road with road features extracted from the current image.…”

Section: Road Priorsmentioning

confidence: 99%

“…However, registration algorithms rely on road information from the current image and are time consuming. The latter, i.e., empirical calibration, consists of learning the parameters from training images and assumes that these parameters do not vary over time [19]. Hence, this method is not usually feasible since the camera undergoes motions due to the vehicle dynamics and road imperfections.…”

Section: Road Priorsmentioning

confidence: 99%

Combining Priors, Appearance, and Context for Road Detection

Álvarez

López

Gevers

et al. 2014

IEEE Trans. Intell. Transport. Syst.

Self Cite

101

View full text Add to dashboard Cite

Abstract-Detecting the free road surface ahead of a moving vehicle is an important research topic in different areas of computer vision, such as autonomous driving or car collision warning. Current vision-based road detection methods are usually based solely on low-level features. Furthermore, they generally assume structured roads, road homogeneity, and uniform lighting conditions, constraining their applicability in real-world scenarios. In this paper, road priors and contextual information are introduced for road detection. First, we propose an algorithm to estimate road priors online using geographical information, providing relevant initial information about the road location. Then, contextual cues, including horizon lines, vanishing points, lane markings, 3-D scene layout, and road geometry, are used in addition to low-level cues derived from the appearance of roads. Finally, a generative model is used to combine these cues and priors, leading to a road detection method that is, to a large degree, robust to varying imaging conditions, road types, and scenarios.

show abstract

Geographic information for vision-based road detection

Cited by 14 publications

References 11 publications

Road recognition from a single image using prior information

Road recognition from a single image using prior information

A Hybrid Vision-Map Method for Urban Road Detection

Combining Priors, Appearance, and Context for Road Detection

Contact Info

Product

Resources

About