Optimal measurement selection algorithm and estimator for ultra-wideband symmetric ranging localization

TL;DR

This paper introduces a versatile state estimator for agents with range, accelerometer, and gyroscope sensors, along with an optimal measurement selection algorithm, validated through indoor multicopter experiments, improving localization accuracy.

Contribution

It presents a novel, agent-agnostic state estimator that does not rely on trilateration and introduces a greedy algorithm for optimal measurement selection.

Findings

The estimator performs well without agent-specific assumptions.

The greedy measurement selection improves localization accuracy.

The algorithm enhances multicopter control in experiments.

Abstract

A state estimator is derived for an agent with the ability to measure single ranges to fixed points in its environment, and equipped with an accelerometer and a rate gyroscope. The state estimator makes no agent-specific assumptions, and can be immediately applied to any rigid body with these sensors. Also, the state estimator doesn't use any trilateration-based method to calculate position from range measurements. As the considered system can only make a single range measurement at a time, we present a greedy optimization algorithm for selecting the best measurement. Experiments in an indoor testbed using an externally controlled multicopter demonstrate the efficacy of the algorithm, specifically showing an improvement over a na\"ive strategy of a fixed sequence of measurements. In separate experiments, the algorithm is also used in feedback control, to control the position of the multicopter.