Efficient motion planning for manipulation robots in environments with deformable objects

Geolocation of a ground object or target of interest from live video is a common task required of small and micro unmanned aerial vehicles (SUAVs and MAVs) in surveillance and rescue applications. However, such vehicles commonly carry low-cost and light-weight sensors providing poor bandwidth and accuracy whose contribution to observations is nonlinear, resulting in poor geolocation performance by standard techniques. This paper proposes the application of an efficient over-parameterized state representation to the problem of geolocation that is able to handle large, time-varying, and non-Gaussian sensor error to produce better geolocation estimates than typical approaches and which provides computing and communication benefits in applications such as predictive control and distributed collaboration. We evaluate our filter on real flight data, demonstrating its ability to efficiently produce a solution with tight confidence bounds given highly uncertain data.