Distance estimation with efference copies and optical flow maneuvers: a stability-based strategy

TL;DR

This paper introduces a stability-based method for estimating distances using monocular optical flow and control inputs, enabling drones to detect proximity and perform autonomous landings by analyzing oscillation patterns.

Contribution

A novel stability-based approach is proposed for distance estimation that leverages self-induced oscillations, applicable during hover and landing, and robust against wind disturbances.

Findings

Successfully estimated distances during hover and landing

Triggered autonomous landing responses based on oscillation detection

Validated approach with simulations and real drone experiments

Abstract

The visual cue of optical flow plays a major role in the navigation of flying insects, and is increasingly studied for use by small flying robots as well. A major problem is that successful optical flow control seems to require distance estimates, while optical flow is known to provide only the ratio of velocity to distance. In this article, a novel, stability-based strategy is proposed to estimate distances with monocular optical flow and knowledge of the control inputs (efference copies). It is shown analytically that given a fixed control gain, the stability of a constant divergence control loop only depends on the distance to the approached surface. At close distances, the control loop first starts to exhibit self-induced oscillations, eventually leading to instability. The proposed stability-based strategy for estimating distances has two major attractive characteristics. First, self-induced oscillations are easy for the robot to detect and are hardly influenced by wind. Second, the distance can be estimated during a zero divergence maneuver, i.e., around hover. The stability-based strategy is implemented and tested both in simulation and with a Parrot AR drone 2.0. It is shown that it can be used to: (1) trigger a final approach response during a constant divergence landing with fixed gain, (2) estimate the distance in hover, and (3) estimate distances during an entire landing if the robot uses adaptive gain control to continuously stay on the 'edge of oscillation'.