# Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2109.14974 · HTML: https://mlsanity.com/paper/2109.14974/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 30 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [Visual-Inertial Odometry of Aerial Robots](https://mlsanity.com/paper/1906.03289/) (2019)
- [Experimental Comparison of Visual-Aided Odometry Methods for Rail Vehicles](https://mlsanity.com/paper/1904.00936/) (2019)
- [VersaVIS: An Open Versatile Multi-Camera Visual-Inertial Sensor Suite](https://mlsanity.com/paper/1912.02469/) (2019)
- [Observability-Aware Trajectory Optimization for Self-Calibration with Application to UAVs](https://mlsanity.com/paper/1604.07905/) (2016)
- [Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning](https://mlsanity.com/paper/2011.02574/) (2020)

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

- [Identifying Policy Gradient Subspaces](https://mlsanity.com/paper/2401.06604/) (2024)
- [Accurate and Interactive Visual-Inertial Sensor Calibration with Next-Best-View and Next-Best-Trajectory Suggestion](https://mlsanity.com/paper/2309.14514/) (2023)
- [Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention Mechanisms](https://mlsanity.com/paper/2503.02913/) (2025)

## Abstract (arXiv)

Visual-inertial sensors have a wide range of applications in robotics. However, good performance often requires different sophisticated motion routines to accurately calibrate camera intrinsics and inter-sensor extrinsics. This work presents a novel formulation to learn a motion policy to be executed on a robot arm for automatic data collection for calibrating intrinsics and extrinsics jointly. Our approach models the calibration process compactly using model-free deep reinforcement learning to derive a policy that guides the motions of a robotic arm holding the sensor to efficiently collect measurements that can be used for both camera intrinsic calibration and camera-IMU extrinsic calibration. Given the current pose and collected measurements, the learned policy generates the subsequent transformation that optimizes sensor calibration accuracy. The evaluations in simulation and on a real robotic system show that our learned policy generates favorable motion trajectories and collects enough measurements efficiently that yield the desired intrinsics and extrinsics with short path lengths. In simulation we are able to perform calibrations 10 times faster than hand-crafted policies, which transfers to a real-world speed up of 3 times over a human expert.