© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

The lack of depth information in camera images has triggered much work on their use for localization and mapping in robotics. In particular, specific landmark parametrizations that isolate the unknown depth in one variable, and that allows to handle the associated large uncertainties have been proposed. Recently, an innovative parametrization (Parallax Angle) has shown to outperform the others in the context of a Bundle Adjustment approach. This paper investigates the way to exploit this parametrization in an incremental graph-based SLAM approach, in a robotics context in which motions measures can be incorporated in the overall estimation. It presents the factors required to initialize landmarks and manage their observations. Simulation results show that the proposed algorithms are able to incrementally incorporate observations, and a discussion analyzes how the incremental updates on ISAM2 are affected by these new factors.

Peer Reviewed