Vision-Based Target Localization for a Flapping-Wing Aerial Vehicle
This addresses the problem of target localization for FWAVs, which have limited load capacity and endurance, but the approach appears incremental as it adapts existing vision-based methods with standard filtering techniques.
The paper tackles target localization for flapping-wing aerial vehicles (FWAVs) by proposing a vision-based algorithm that uses a generic camera model and a first-order low-pass filter to handle noise and jitter. Simulation results show the algorithm performs well, though no specific accuracy numbers are provided.
The flapping-wing aerial vehicle (FWAV) is a new type of flying robot that mimics the flight mode of birds and insects. However, FWAVs have their special characteristics of less load capacity and short endurance time, so that most existing systems of ground target localization are not suitable for them. In this paper, a vision-based target localization algorithm is proposed for FWAVs based on a generic camera model. Since sensors exist measurement error and the camera exists jitter and motion blur during flight, Gaussian noises are introduced in the simulation experiment, and then a first-order low-pass filter is used to stabilize the localization values. Moreover, in order to verify the feasibility and accuracy of the target localization algorithm, we design a set of simulation experiments where various noises are added. From the simulation results, it is found that the target localization algorithm has a good performance.