Real Time Localization and 3D Reconstruction

E. Mouragnon; Maxime Lhuillier; Michel Dhome; Fabien Dekeyser; Patrick Sayd

doi:10.1109/cvpr.2006.236

Abstract

In this paper we describe a method that estimates the motion of a calibrated camera (settled on an experimental vehicle) and the tridimensional geometry of the environment. The only data used is a video input. In fact, interest points are tracked and matched between frames at video rate. Robust estimates of the camera motion are computed in real-time, key-frames are selected and permit the features 3D reconstruction. The algorithm is particularly appropriate to the reconstruction of long images sequences thanks to the introduction of a fast and local bundle adjustment method that ensures both good accuracy and consistency of the estimated camera poses along the sequence. It also largely reduces computational complexity compared to a global bundle adjustment. Experiments on real data were carried out to evaluate speed and robustness of the method for a sequence of about one kilometer long. Results are also compared to the ground truth measured with a differential GPS.

Keywords

Bundle adjustmentComputer visionRobustness (evolution)Artificial intelligenceComputer scienceGround truthGlobal Positioning SystemMotion estimationStructure from motionSingle camera3D reconstructionPhotogrammetry

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Publication Info

Year: 2006
Type: preprint
Pages: 363-370
Citations: 372
Access: Closed

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APA Style

                            
                                    E. Mouragnon, 
                                
                                    Maxime Lhuillier, 
                                
                                    Michel Dhome
                                
                                et al.
                            
                            (2006). 
                            Real Time Localization and 3D Reconstruction. 
                            2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)
                            
                            , 363-370.
                            https://doi.org/10.1109/cvpr.2006.236

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DOI: 10.1109/cvpr.2006.236

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Data completeness: 81%