Robust Airborne 3D Visual Simultaneous Localization and Mapping with Observability and Consistency Analysis

This paper aims to present a robust airborne 3D Visual Simultaneous Localization and Mapping (VSLAM) solution based on a stereovision system. We propose three innovative contributions to the Airborne VSLAM. The first one is the development of an alternative data fusion nonlinear H a filtering scheme. This scheme is based on 3D vision observation model and avoids issues linked with the classical Extended Kalman Filtering (EKF) techniques such as the linearization errors, the initialization problem and noise statistics assumptions. The second contribution consists of a consistency and observability analysis for the Airborne VSLAM. The third contribution is a new approach to map management, based on the k-nearest landmark concept, and allowing efficient loop closure detection and map building. This approach reduces considerably the complexity of our Airborne VSLAM algorithm, which becomes independent of the map landmark number. Simulation results show the efficiency of the proposed Airborne VSLAM solution for which comparisons with other techniques are favourable.