Uncertainty, Baseline, and Noise Analysis for L1 Error-Based Multi-view Triangulation

Abstract

A comprehensive uncertainty, baseline, and noise analysis in computing 3D points using a recent L1-based triangulation algorithm is presented. This method is shown to be not only faster and more accurate than its main competitor, linear triangulation, but also more stable under noise and baseline changes. A Monte Carlo analysis of covariance and a confidence ellipsoid analysis were performed over a large range of baselines and noise levels for different camera configurations, to compare performance between angular error-based and linear triangulation. Furthermore, the effect of baseline and noise was analyzed for true multi-view triangulation versus pairwise stereo fusion. Results on real and synthetic data show that L1 angular error-based triangulation has a positive effect on confidence ellipsoids, lowers covariance values and results in more-accurate pairwise and multi-view triangulation, for varying numbers of cameras and configurations.

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