Observability-Aware Trajectory Optimization: Theory, Viability, and State of the Art
This work addresses the problem of designing robot trajectories for better estimation, but it is incremental as it focuses on comparing and clarifying existing methods.
The paper compares two state-of-the-art methods for observability-aware trajectory optimization to improve robot state and parameter estimation, evaluating them on sensor-to-sensor extrinsic self-calibration in a simulator to assess their viability and sensitivity.
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.