Free Space in Front of an Autonomous Guided Vehicle in Inner-City Conditions

Simond, Nicolas

doi:10.1007/978-3-540-75867-9_135

Cited by 4 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(5) Otherwise, if the size of sub-set S i is below T , a new random sample is selected and steps 2, 3 and 4 are repeated. (6) After N iterations (maximum number of trials), draw sub-set S ic yielding the largest consensus (greatest number of "inliers"). The model is finally estimated using all points belonging to S ic .…”

Section: Random Sample Consensusmentioning

confidence: 99%

See 1 more Smart Citation

Robust visual odometry for vehicle localization in urban environments

et al. 2009

View full text Add to dashboard Cite

show abstract

Section: Random Sample Consensusmentioning

confidence: 99%

“…[1][2][3]. [6], trajectory estimation is carried out using visual cues for the sake of autonomously driving a car in inner-city conditions. 4 In ref.…”

Section: Introductionmentioning

confidence: 99%

Robust visual odometry for vehicle localization in urban environments

et al. 2009

View full text Add to dashboard Cite

show abstract

“…The system was tested in outdoor scenarios on different runs of up to 150 meters each. In [6], trajectory estimation is carried out using visual cues for the sake of autonomously driving a car in inner-city conditions.…”

Section: Introductionmentioning

confidence: 99%

Robust visual odometry for complex urban environments

Parra

Sotelo

Vlacic

2008

2008 IEEE Intelligent Vehicles Symposium

View full text Add to dashboard Cite

This paper describes a new approach for estimating the vehicle motion trajectory in complex urban environments by means of visual odometry. A new strategy for robust feature extraction and data post-processing is developed and tested on-road. Scale-invariant Image Features (SIFT) are used in order to cope with the complexity of urban environments. The obtained results are discussed and compared to previous works. In the prototype system, the ego-motion of the vehicle is computed using a stereo-vision system mounted next to the rear view mirror of the car. Feature points are matched between pairs of frames and linked into 3D trajectories. The distance between estimations is dynamically adapted based on reprojection and estimation errors. Vehicle motion is estimated using the non-linear, photogrametric approach based on RANSAC (RAndom SAmple Consensus). The obvious application of the method is to provide on-board driver assistance in navigation tasks, or to provide a means of autonomously navigating a vehicle. The method has been tested in real traffic conditions without using prior knowledge about the scene or the vehicle motion. An example of how to estimate a vehicle's trajectory is provided along with suggestions for possible further improvement of the proposed odometry algorithm.

show abstract

3D Visual Odometry for Road Vehicles

García

Sotelo

Parra

et al. 2007

J Intell Robot Syst

View full text Add to dashboard Cite

This paper describes a method for estimating the vehicle global position in a network of roads by means of visual odometry. To do so, the ego-motion of the vehicle relative to the road is computed using a stereo-vision system mounted next to the rear view mirror of the car. Feature points are matched between pairs of frames and linked into 3D trajectories. Vehicle motion is estimated using the non-linear, photogrametric approach based on RANSAC. This iterative technique enables the formulation of a robust method that can ignore large numbers of outliers as encountered in real traffic scenes. The resulting method is defined as visual odometry and can be used in conjunction with other sensors, such as GPS, to produce accurate estimates of the vehicle global position. The obvious application of the method is to provide on-board driver assistance in navigation tasks, or to provide a means for autonomously navigating a vehicle. The method has been tested in real traffic conditions without using prior knowledge about the scene nor the vehicle motion. We provide examples of estimated vehicle trajectories using the proposed method and discuss the key issues for further improvement.

show abstract

Free Space in Front of an Autonomous Guided Vehicle in Inner-City Conditions

Cited by 4 publications

References 2 publications

Robust visual odometry for vehicle localization in urban environments

Robust visual odometry for vehicle localization in urban environments

Robust visual odometry for complex urban environments

3D Visual Odometry for Road Vehicles

Contact Info

Product

Resources

About