STEAP: simultaneous trajectory estimation and planning

Simultaneous Trajectory Estimation and Planning via Probabilistic Inference

We provide a unified probabilistic framework for trajectory estimation and planning. The key idea is to view these two problems, usually considered separately, as a single problem. At each time-step the robot is tasked with finding the complete continuous-time trajectory from start to goal. This can be quite difficult; the robot must contend with a potentially high-degreeof-freedom (DOF) trajec...

STEAP: Towards Online Estimation and Replanning

In this work, we present simultaneous trajectory estimation and planning (STEAP) a unified approach to solving continuous-time trajectory estimation and planning problems. Although, these problems are usually considered separately, within our framework we show how estimation and planning can benefit from each other and remove redundancy during computation. Each time-step the robot is tasked wit...

Detection via simultaneous trajectory estimation and long time integration

In this work, we consider the detection of manoeuvring small objects with radars. Such objects induce low signal to noise ratio (SNR) reflections in the received signal. We consider both co-located and separated transmitter/receiver pairs, i.e., mono-static and bi-static configurations, respectively, as well as multi-static settings involving both types. We propose a detection approach which is...

Simultaneous State Estimation of UAV Trajectory Using Probabilistic Graph Models

For many airborne sensor applications, knowledge of the aerial platform’s position and velocity assists in construction of the target image and mapping the resultant model onto a surveyed map. Since sensor data is processed during post-processing, emphasis is placed on an accurate trajectory estimate as opposed to a real-time navigation. In this paper we present a smoothing approach for integra...

Trajectory and Task Planning