Detection and characterization of moving objects with aerial vehicles using inertial-optical flow

In this paper, we present a novel approach in combining visual and inertial measurements in non-static environments for first order characterization of the metric motion of non-static objects in the scene. Our approach leverages online estimated ego motion states and uses a novel inertial-optical flow (IOF) measurement analysis to identify moving objects and to characterize them in their angular and linear velocities. The novelty of our algorithm lies in the identification and segmentation of consistent optical flow outliers in the so-called kinematic space. These consistent outliers in combination with the IOF information for ego-motion estimation yield a first order estimation of the moving object in full 3D and in metric units. The approach is highly efficient as it only requires matched features in two consecutive images. We evaluate and demonstrate our algorithm in simulations and in real world tests.