Incremental smoothing vs. filtering for sensor fusion on an indoor UAV

Our paper explores the performance of a recently proposed incremental smoother in the context of nonlinear sensor fusion for a real-world UAV. This efficient factor graph based smoothing approach has a number of advantages compared to conventional filtering techniques like the EKF or its variants. It can more easily incorporate asynchronous and delayed measurements from sensors operating at different rates and is supposed to be less error-prone in highly nonlinear settings. We compare the novel incremental smoothing approach based on iSAM2 against our conventional EKF based sensor fusion framework. Unlike previously presented work, the experiments are not only performed in simulation, but also on a real-world quadrotor UAV system using IMU, optical flow and altitude measurements.