Estimation of Displacements from Two 3-D Frames Obtained From Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Shape Indexing Using Approximate Nearest-Neighbour Search in High-Dimensional Spaces
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Real-time Localization in Outdoor Environments using Stereo Vision and Inexpensive GPS
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
3D Visual Odometry for Road Vehicles
Journal of Intelligent and Robotic Systems
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Free space in front of an autonomous guided vehicle in inner-city conditions
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Combination of Feature Extraction Methods for SVM Pedestrian Detection
IEEE Transactions on Intelligent Transportation Systems
Automatic vehicle identification for Argentinean license plates using intelligent template matching
Pattern Recognition Letters
Estimation and prediction of the vehicle's motion based on visual odometry and kalman filter
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
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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. Images from scale-invariant feature transform (SIFT) features 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 re-projection and estimation errors. Vehicle motion is estimated using the non-linear, photogrametric approach based on RAndom SAmple Consensus (RANSAC). The final goal 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.