Combined lane and road attributes extraction by fusing data from digital map, laser scanner and camera

Tsogas, Manolis; Floudas, N.; Lytrivis, Panagiotis; Amditis, Angelos; Polychronopoulos, A.

doi:10.1016/j.inffus.2010.01.005

Cited by 27 publications

(11 citation statements)

References 18 publications

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“…Whereas, laser scanner solutions are usually faster because of the wide range of possible distances. Laser scanners can be found in numerous recent solutions connected with human detection and tracking [42], road and lane detection, and description [43] or automotive applications for vehicle motion controlling [44]. Depth information sensors are very accurate, especially for short distances, where vision-based solutions produce poor results.…”

Section: Related Workmentioning

confidence: 99%

Improved Human Detection with a Fusion of Laser Scanner and Vision/Infrared Information for Mobile Applications

2018

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This paper presents a method for human detection using a laser scanner with vision or infrared images. Mobile applications require reliable and efficient methods for human detection, especially as a part of driver assistance systems, including pedestrian collision systems. The authors propose an efficient method for multimodal human detection based on a combination of the features and context information. Strictly, the human is detected in the vision/infrared images using a combination of local binary patterns and histogram of oriented gradients features with a neural network in a cascade manner. Next, using coordinates of detected humans from the vision system, the moving trajectory is predicted until the scanner working distance is reached by the individual human. Then the segmentation of data from the laser scanner is further carried out with respect to the predicted trajectory. Finally, human detection in the laser scanner working distance is performed based on modelling of the human legs. The modelling is based on the adaptive breakpoint detection algorithm and proposed improved polylines definition and fitting algorithm. The authors conducted a set of experiments in predefined scenarios, discussed the identified weakness and advantages of the proposed method, and outlined detailed future work, especially for night-time and low-light conditions.

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Section: Related Workmentioning

confidence: 99%

Improved Human Detection with a Fusion of Laser Scanner and Vision/Infrared Information for Mobile Applications

2018

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“…Digital map data can help to further extend the range of the road course estimation even beyond the distance where radar sensors can take measurements. Tsogas et al [8] fuse measurements of a digital map, laser scanner and a camera, based on the clothoid model. A Sugeno-fuzzy system chooses an appropriate weighting of the different sensors for every distance.…”

Section: B Related Workmentioning

confidence: 99%

“…Such violations of the monotonies (6,7,8) can have a negative effect later on in the fusion system. Because of the monotone relation, the underlying functions are modeled as polynomial planes.…”

Section: Continuous Approximation Of Parametersmentioning

confidence: 99%

Probabilistic fusion of rural road course estimations

Schule

Koch

Hartmann

et al. 2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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This paper presents an advanced road course prediction algorithm focusing on longer distances. It shows how to simply combine the different sensors available in modern cars for a road course estimation task. Concretely, a digital-map-based estimation is fused with an optical lane recognition system. Both sensors are evaluated on a representative subset of test sequences to characterize their measurement uncertainties. Then a Bayesian fusion system combines the advantages of the single sensors. Extensive evaluations with high precision ground truth data demonstrate the feasibility of this approach.

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“…Commercial image-based road extraction systems have been widely available (Liu et al 2013). However, image-based mobile mapping systems are limited to illumination and weather conditions, shadows cast by roadside buildings and trees, and occlusions caused by nearby moving vehicles and pedestrians (Tsogas et al 2011). Moreover, the captured image data lack of accurate geospatial information and suffer greatly from distortions.…”

Section: Introductionmentioning

confidence: 99%

Use of mobile LiDAR in road information inventory: a review

Guan

Cao

et al. 2016

International Journal of Image and Data Fusion

179

119

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Mobile LiDAR technology is currently one of the attractive topics in the fields of remote sensing and laser scanning. Mobile LiDAR enables a rapid collection of enormous volumes of highly dense, irregularly distributed, accurate geo-referenced data, in the form of three-dimensional (3D) point clouds. This technology has been gaining popularity in the recognition of roads and road-scene objects. A thorough review of available literature is conducted to inform the advancements in mobile LiDAR technologies and their applications in road information inventory. The literature review starts with a brief overview of mobile LiDAR technology, including system components, direct geo-referencing, data error analysis and geometrical accuracy validation. Then, this review presents a more in-depth description of current mobile LiDAR studies on road information inventory, including the detection and extraction of road surfaces, small structures on the road surfaces and polelike objects. Finally, the challenges and future trends are discussed. Our review demonstrates the great potential of mobile LiDAR technology in road information inventory. ARTICLE HISTORY

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Combined lane and road attributes extraction by fusing data from digital map, laser scanner and camera

Cited by 27 publications

References 18 publications

Improved Human Detection with a Fusion of Laser Scanner and Vision/Infrared Information for Mobile Applications

Improved Human Detection with a Fusion of Laser Scanner and Vision/Infrared Information for Mobile Applications

Probabilistic fusion of rural road course estimations

Use of mobile LiDAR in road information inventory: a review

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