This paper proposes a novel method to estimate the scale online for monocular SLAM. Unlike conventional scale estimation methods that require a metric sensor such as an IMU and apriori knowledge of its biases, this approach estimates the scale online by solely using the monocular camera and multirotor dynamics model in an extended Kalman Filter framework. Further, we discuss the observability of scale and theoretically show that the scale becomes observable when multirotor dynamics model and monocular vision are used in conjunction. We validate our proposition with extensive experimentation on the local as well as on the standard datasets and compare the same with other state of the art methods.

This paper presents a novel method to eliminate drift for dynamics model based localization of multirotors. The dynamics equations require drag modelling, which is dependent on velocity, to generate vehicles's acceleration along the body axis. It is shown in this paper that the drag contribution, at velocity level, can be treated as a low frequency component. Incorrect or nonmodelling of this low frequency component leads to drift at velocity level. This drift can then be removed through a high pass filter. Experiments are performed on an AR.Drone quadrotor platform and results validated using motion capture camera system. The obtained results demonstrate that treating drag in the said manner leads to drift free velocity and hence to better localization. Further, it is shown that this approach could be exploited to extract scale information solely from a monocular camera.