Error Analysis in Stereo Determination of 3-D Point Positions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Parameter Estimation in Computer Vision
SIAM Review
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
An Improved Calibration Technique for Coupled Single-Row Telemeter and CCD Camera
3DIM '05 Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling
Visualizing Quaternions (The Morgan Kaufmann Series in Interactive 3D Technology)
Visualizing Quaternions (The Morgan Kaufmann Series in Interactive 3D Technology)
Simultaneous Localization, Mapping and Moving Object Tracking
International Journal of Robotics Research
Multisensor Data Fusion
A new approach to urban pedestrian detection for automatic braking
IEEE Transactions on Intelligent Transportation Systems
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Much work is currently devoted to increasing the reliability, completeness and precision of the data used by driving assistance systems, particularly in urban environments. Urban environments represent a particular challenge for the task of perception, since they are complex, dynamic and completely variable. This article examines a multi-modal perception approach for enhancing vehicle localization and the tracking of dynamic objects in a world-centric map. 3D ego-localization is achieved by merging stereo vision perception data and proprioceptive information from vehicle sensors. Mobile objects are detected using a multi-layer lidar that is simultaneously used to identify a zone of interest to reduce the complexity of the perception process. Object localization and tracking is then performed in a fixed frame which simplifies analysis and understanding of the scene. Finally, tracked objects are confirmed by vision using 3D dense reconstruction in focused regions of interest. Only confirmed objects can generate an alarm or an action on the vehicle. This is crucial to reduce false alarms that affect the trust that the driver places in the driving assistance system. Synchronization issues between the sensing modalities are solved using predictive filtering. Real experimental results are reported so that the performance of the multi-modal system may be evaluated.