A direct optic flow-based strategy for inverse flight altitude estimation with monocular vision and IMU measurements

Abstract

With tiny and limited nervous systems, insects demonstrate a remarkable ability to fly through complex environments. Optic flow has been identified to play a crucial role in regulating flight conditions and navigation in flies and bees. In robotics, optic flow has been widely studied thanks to the low computational requirements. However, with only monocular visual information, optic flow is inherently devoid of a scale factor required for estimating the absolute distance. In this paper, we propose a strategy for estimating the flight altitude of a flying robot with a ventral camera by combining the optic flow with measurements from an inertial measurement unit. Instead of using the prevalent feature-based approach for calculation of optic flow, we implement a direct method that evaluates the flow information via image gradients. We show that the direct approach notably simplifies the computation steps compared to the feature-based method. When combined with an extended Kalman filter for fusion of inertial measurement units measurements, the flight altitude can be estimated in real time. We carried out extensive flight tests in different settings. Among 31 hovering and vertical flights near the altitude of 40 cm, we achieved the RMS errors in the altitude estimate of 2.51 cm. Further analysis of factors that affect the quality of the flow and the distance estimate is also provided. PAPER Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.

Direct Visual-Inertial Ego-Motion Estimation Via Iterated Extended Kalman Filter

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“…This is an attractive compromise when landmarks and the corresponding depth map is not considered. The assumption, also present in [11], [12], [17], [18], radically simplifies the computation, eliminating the preference to consider image patches to reduce the computational complexity as found in recent map-based strategies [15], [16]. Furthermore, the proposed approach has no restriction on the motion pattern of the camera or the plane's inclination as present in [12], [17], [18].…”

A Quadrotor With an Origami-Inspired Protective Mechanism

“…In regard to the scalability of the required triggering force, equation (3) suggests that the ratio of the trigger force (F) to the robot's weight (T ) is independent of size as both d and x on the right hand side of equation ( 3) are expected to scale with the characteristic length (l) of the robot. In other words, we anticipate F ∼ T or F ∼ l 3 .…”

“…Recent rapid developments of aerial robots have shown promise. Following the advances in flight dynamics and control [1], planning and localization [2], [3], etc., there emerge numerous applications of these small flying robots that involve interactions of robots with objects or environments. These include, for instance, transportation of a suspended payload [4], climbing on a vertical surface [5], perching on an overhang [6], [7], aerial manipulation [8].…”

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