# Accurate and Interactive Visual-Inertial Sensor Calibration with Next-Best-View and Next-Best-Trajectory Suggestion

arXiv: https://arxiv.org/abs/2309.14514 · HTML: https://mlsanity.com/paper/2309.14514/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 25 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [A micro Lie theory for state estimation in robotics](https://mlsanity.com/paper/1812.01537/) (2018)
- [On 'A Kalman Filter-Based Algorithm for IMU-Camera Calibration: Observability Analysis and Performance Evaluation'](https://mlsanity.com/paper/1311.4769/) (2013)
- [A Primer on the Differential Calculus of 3D Orientations](https://mlsanity.com/paper/1606.05285/) (2016)
- [Visual-inertial self-calibration on informative motion segments](https://mlsanity.com/paper/1708.02382/) (2017)
- [Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning](https://mlsanity.com/paper/2109.14974/) (2021)
- [Observability-aware Self-Calibration of Visual and Inertial Sensors for Ego-Motion Estimation](https://mlsanity.com/paper/1901.07242/) (2019)
- [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, 1 of 1)

- [Active 6D Pose Estimation for Textureless Objects using Multi-View RGB Frames](https://mlsanity.com/paper/2503.03726/) (2025)

## Abstract (arXiv)

Visual-Inertial (VI) sensors are popular in robotics, self-driving vehicles, and augmented and virtual reality applications. In order to use them for any computer vision or state-estimation task, a good calibration is essential. However, collecting informative calibration data in order to render the calibration parameters observable is not trivial for a non-expert. In this work, we introduce a novel VI calibration pipeline that guides a non-expert with the use of a graphical user interface and information theory in collecting informative calibration data with Next-Best-View and Next-Best-Trajectory suggestions to calibrate the intrinsics, extrinsics, and temporal misalignment of a VI sensor. We show through experiments that our method is faster, more accurate, and more consistent than state-of-the-art alternatives. Specifically, we show how calibrations with our proposed method achieve higher accuracy estimation results when used by state-of-the-art VI Odometry as well as VI-SLAM approaches. The source code of our software can be found on: https://github.com/chutsu/yac.