# Observability-Aware Trajectory Optimization for Self-Calibration with Application to UAVs

arXiv: https://arxiv.org/abs/1604.07905 · HTML: https://mlsanity.com/paper/1604.07905/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2016.

## Built on by (most cited first, 3 of 3)

- [Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning](https://mlsanity.com/paper/2109.14974/) (2021)
- [Enhancing Feature Tracking Reliability for Visual Navigation using Real-Time Safety Filter](https://mlsanity.com/paper/2502.01092/) (2025)
- [Structural Foundations of Nonlinear Systems with Unknown Inputs: The UID-Induced Normal Form and Minimal-Sensing Structure-from-Motion](https://mlsanity.com/paper/2610.05939/) (2026)

## Abstract (arXiv)

We study the nonlinear observability of a systems states in view of how well they are observable and what control inputs would improve the convergence of their estimates. We use these insights to develop an observability-aware trajectory-optimization framework for nonlinear systems that produces trajectories well suited for self-calibration. Common trajectory-planning algorithms tend to generate motions that lead to an unobservable subspace of the system state, causing suboptimal state estimation. We address this problem with a method that reasons about the quality of observability while respecting system dynamics and motion constraints to yield the optimal trajectory for rapid convergence of the self-calibration states (or other user-chosen states). Experiments performed on a simulated quadrotor system with a GPS-IMU sensor suite demonstrate the benefits of the optimized observability-aware trajectories when compared to a covariance-based approach and multiple heuristic approaches. Our method is approx. 80x faster than the covariance-based approach and achieves better results than any other approach in the self-calibration task. We applied our method to a waypoint navigation task and achieved a approx. 2x improvement in the integrated RMSE of the global position estimates and approx. 4x improvement in the integrated RMSE of the GPS-IMU transformation estimates compared to a minimal-energy trajectory planner.