This paper addresses the problem of motion estimation and 3-D reconstruction through visual tracking with a single-viewpoint sensor and, in particular, how to generalize tracking to calibrated omnidirectional cameras. We analyze different minimization approaches for the intensity-based cost function (sum of squared differences). In particular, we propose novel variants of the efficient second-order minimization (ESM) with better computational complexities and compare these algorithms with the inverse composition (IC) and the hyperplane approximation (HA). Issues regarding the use of the IC and HA for 3-D tracking are discussed. We show that even though an iteration of ESM is computationally more expensive than an iteration of IC, the faster convergence rate makes it globally faster. The tracking algorithm was validated by using an omnidirectional sensor mounted on a mobile robot.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper addresses the problem of motion estimation and 3-D reconstruction through visual tracking with a single-viewpoint sensor and, in particular, how to generalize tracking to calibrated omnidirectional cameras. We analyze different minimization approaches for the intensity-based cost function (sum of squared differences). 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