Relative Constraints and Their Contribution to Image Configurations

Abstract

Simultaneous localization of a group of mobile platforms has seen growing interest in recent years. It is common in such setups to equip each platform with a high-end inertial measurement unit and an imaging system. Notwithstanding, present research into such platforms’ dynamics tends to introduce some forms of relaxation, e.g., planar motion, ideal inertial sensors, or a static and near-sight scene. Yet, such assumptions are violated in real-world scenarios, for example, cars driving on bumpy roads, or operating with low-performance inertial sensors. In this paper, we propose to improve the navigation solution of each member in the mobile platforms group and reflect actual operational conditions by the introduction of stochastic constraints on the relative pose of the platforms. Our framework improves the accuracy of the navigation solution and demonstrates greater flexibility in mission–planning and a shorter time on site. Our results show that accurate pose estimates are maintained even when the imaging scale is increased, allowing greater coverage and shorter operation time, which in turn reduces costs. This is demonstrated by an analytical assessment, simulations, and real–world experiments on a group of platforms equipped with low–cost, off–the–shelf sensors.