Robust curb detection and vehicle localization in urban environments

Hata, Alberto Yukinobu; Osório, Fernando Santos; Wolf, Denis F.

doi:10.1109/ivs.2014.6856405

Cited by 115 publications

(68 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The experiment showed that the longitudinal error was the major responsible for the localization error (mean 1.49 meters), despite of a relatively low lateral error of approximately 0.52 meter (14) .…”

Section: Curb Based Localizationmentioning

confidence: 95%

“…In (4) and (14), curb like obstacles can be detected by analyzing the ring compression of a multilayer LIDAR as shown in Fig 5. However, when obstacles as pedestrians and cars are present in the street, the object will possibly be detected as curbs. The regression filter was introduced to estimate the curb shape and to remove points that do not follow the road model (14) .…”

Section: Curb Based Localizationmentioning

confidence: 99%

See 1 more Smart Citation

Autonomous Vehicle Technologies :Localization and Mapping

俊介

艶磊

立達

2015

Fundamentals Review

View full text Add to dashboard Cite

Vehicle self-localization is an important and challenging issue in current driving assistance and autonomous driving research activities. This paper mainly investigates two kinds of methods for vehicle self-localization: active sensor based and passive sensor based. Active sensor based localization was firstly proposed for robot localization, and was introduced into autonomous driving recently. The Simultaneous Localization and Mapping (SLAM) techniques is the representative in active sensor based localization. The passive sensor based localization technologies are categorized and explained based on the type of sensors, Global Navigation Satellite System (GNSS), inertial sensors and cameras. In this paper, researches utilizing active sensors and passive sensors for autonomous vehicles are reviewed extensively. Finally, our challenges on self-localization in urban canyon by the system integration of passive sensors is introduced. GNSS error has been improved for the purpose of the self-localization in urban canyon. The performance of the proposed method would suggest possible solution autonomous vehicles which makes use of passive sensors more.

show abstract

Section: Curb Based Localizationmentioning

confidence: 95%

Section: Curb Based Localizationmentioning

confidence: 99%

Autonomous Vehicle Technologies :Localization and Mapping

俊介

艶磊

立達

2015

Fundamentals Review

View full text Add to dashboard Cite

show abstract

“…Method II: Study of the separation between intersecting sections of consecutive laser scanner layers This method is based on [27]. Considering the intersections of the layers of the laser scanner with the ground surface, the radius of the circumferences (assuming perpendicularity between the vertical axis of the laser scanner and the ground) depends on the height, so considering that the roadway is not in the same vertical dimension as the adjacent zones, a gradient in the radius of said intersection curves can be expected.…”

Section: Road Boundaries Detection Functionsmentioning

confidence: 99%

Accurate and Detailed Transversal Road Section Characteristics Extraction Using Laser Scanner

et al. 2018

View full text Add to dashboard Cite

Road vehicle lateral positioning is a key aspect of many assistance applications and autonomous driving. However, conventional GNSS-based positioning systems and fusion with inertial systems are not able to achieve these levels of accuracy under real traffic conditions. Onboard perception systems provide knowledge of the surroundings of the vehicle, and some algorithms have been proposed to detect road boundaries and lane lines. This information could be used to locate the vehicle in the lane. However, most proposed algorithms are quite partial and do not take advantage of a complete knowledge of the road section. This paper proposes an integrated approach to the two tasks that provides a higher level of robustness of results: road boundaries detection and lane lines detection. Furthermore, the algorithm is not restricted to certain scenarios such as the detection of curbs; it could be also used in off-road tracks. The functions have been tested in real environments and their capabilities for autonomous driving have been verified. The algorithm is ready to be merged with digital map information; this development would improve results accuracy.

show abstract

“…Zhao [26] obtained the seed points that meet three criteria and modeled the curb as a parabola while using a random sample consensus (RANSAC) algorithm to remove the false positives that do not match the parabolic model. Least trimmed squares (LTS) regression method is applied to fit a function in the curb candidate points in [27] while a sliding window is applied to obtain curb points in [28]. In the study of Liu et al [24], distance transformation is performed on the obstacle map and threshold segmentation is used to increase the continuity of the map.…”

Section: Introductionmentioning

confidence: 99%

A Practical Point Cloud Based Road Curb Detection Method for Autonomous Vehicle

et al. 2017

View full text Add to dashboard Cite

Robust and quick road curb detection under various situations is critical in developing intelligent vehicles. However, the road curb detection is easily affected by the obstacles in the road area when Lidar based method is applied. A practical road curb detection method using point cloud from a three-dimensional Lidar for autonomous vehicle is reported in this paper. First, a multi-feature, loose-threshold, varied-scope ground segmentation method is presented to increase the robustness of ground segmentation with which obstacles above the ground can be detected. Second, the road curb is detected by applying the global road trend and an extraction-update mechanism. Experiments show the robustness and efficiency of the road curb detection under various environments. The road curb detection method is 10 times the speed of traditional method and the accuracy is much higher than existing methods.

show abstract

Robust curb detection and vehicle localization in urban environments

Cited by 115 publications

References 8 publications

Autonomous Vehicle Technologies :Localization and Mapping

Autonomous Vehicle Technologies :Localization and Mapping

Accurate and Detailed Transversal Road Section Characteristics Extraction Using Laser Scanner

A Practical Point Cloud Based Road Curb Detection Method for Autonomous Vehicle

Contact Info

Product

Resources

About