TASP MSc Seminar – Dror Hurwitz – “Autonomous Mapping Using Quadcopter Formation”

Work towards MSc degree under the supervision of Assoc. Prof. Sagi Filin and Dr. Iztik Klein

When: 1.12.2020 at 14:30

Where: Zoom

Abstract: Simultaneous localization and mapping for 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 (IMU), an imaging system, and as long as outdoor applications are concerned, a global navigation satellite system receiver. In most cases some form of relaxation is introduced e.g., planner motion assumptions, ideal IMUs, and a static and near-sight scene is common. Yet, many mobile platforms do not follow these assumptions, particularly with a six degrees of freedom (DOF) dynamics of airborne platforms, but also for vehicles operating on roads with bumps, pedestrians navigating with low-cost mobile phone sensors, or maritime platforms. In this research, we propose to improve the navigation solution, and the quality of the consequent mapping by reflecting actual operational conditions, better estimation of the sensor errors, and introduction stochastic constraints on the relative pose of the platforms.  To make our solution viable and affordable, we study use of low-cost sensors to facilitate such constraints and manners by which they can be integrated. We propose a model that manages to improve the accuracy of the navigation solution, to enhance the numerical stability and robustness of the estimator, and to improve the accuracy of the forward projection. We show that our model allows for a greater flexibility in the mission planning and facilitates shorter time on site without compromising quality concerns. Our results show that high level of accuracy is maintained even when the scale is increased, allowing to achieve greater coverage and shorter operation time which in turn reduces costs. This is demonstrated by both simulations and real-world experiments on a group of platforms equipped with low-cost off-the-shelf sensors. As we show, our model is general and flexible, allowing to accommodate with different types of sensors and platforms.