Error Propagations for Local Bundle Adjustment

Alexandre Eudes (1, 2) , Maxime Lhuillier (3)

1 LASMEA - Laboratoire des sciences et matériaux pour l'électronique et d'automatique
2 LIST (CEA) - Laboratoire d'Intégration des Systèmes et des Technologies
3 IP - Institut Pascal

Local bundle adjustment (LBA) has recently been introduced to estimate the geometry of image sequences taken by a calibrated camera. Its advantage over standard (global) bundle adjustment is a great reduction of computational complexity, which allows real-time performances with a similar accuracy. However, no confidence measure on the LBA result such as uncertainty or covariance has yet been introduced. This paper introduces statistical models and estimation methods for uncertainty with two desirable properties: (1) uncertainty propagation along the sequence and (2) real-time calculation. We also explain why this problem is more complicated than it may appear at first glance, and we provide results on video sequences.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Alexandre Eudes, Maxime Lhuillier. Error Propagations for Local Bundle Adjustment. IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009), IEEE, Jun 2009, Miami, FL, United States. ⟨10.1109/CVPR.2009.5206824⟩. ⟨hal-01635667⟩