Observability-Aware Trajectory Optimization: Theory, Viability, and State of the Art

TL;DR

This paper compares two leading observability-aware trajectory optimization methods, providing theoretical insights and evaluating their effectiveness in sensor calibration tasks using realistic simulations.

Contribution

It offers a comparative analysis of state-of-the-art methods, clarifies their theoretical foundations, and assesses their practical performance in sensor calibration scenarios.

Findings

Both methods improve state estimation accuracy.

Sensitivity to sensor measurement information varies between methods.

Theoretical clarifications enhance understanding of method efficacy.

Abstract

Ideally, robots should move in ways that maximize the knowledge gained about the state of both their internal system and the external operating environment. Trajectory design is a challenging problem that has been investigated from a variety of perspectives, ranging from information-theoretic analyses to leaning-based approaches. Recently, observability-based metrics have been proposed to find trajectories that enable rapid and accurate state and parameter estimation. The viability and efficacy of these methods is not yet well understood in the literature. In this paper, we compare two state-of-the-art methods for observability-aware trajectory optimization and seek to add important theoretical clarifications and valuable discussion about their overall effectiveness. For evaluation, we examine the representative task of sensor-to-sensor extrinsic self-calibration using a realistic physics simulator. We also study the sensitivity of these algorithms to changes in the information content of the exteroceptive sensor measurements.