# Improving Monocular Visual-Inertial Initialization with Structureless Visual-Inertial Bundle Adjustment

arXiv: https://arxiv.org/abs/2502.16598 · HTML: https://mlsanity.com/paper/2502.16598/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 23 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [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)
- [The TUM VI Benchmark for Evaluating Visual-Inertial Odometry](https://mlsanity.com/paper/1804.06120/) (2018)
- [On-Manifold Preintegration for Real-Time Visual-Inertial Odometry](https://mlsanity.com/paper/1512.02363/) (2015)
- [Quaternion kinematics for the error-state Kalman filter](https://mlsanity.com/paper/1711.02508/) (2017)
- [Inertial-Only Optimization for Visual-Inertial Initialization](https://mlsanity.com/paper/2003.05766/) (2020)
- [Range-Visual-Inertial Odometry: Scale Observability Without Excitation](https://mlsanity.com/paper/2103.15215/) (2021)
- [SchurVINS: Schur Complement-Based Lightweight Visual Inertial Navigation System](https://mlsanity.com/paper/2312.01616/) (2023)
- [Closed-form Preintegration Methods for Graph-based Visual-Inertial Navigation](https://mlsanity.com/paper/1805.02774/) (2018)
- [Learned Monocular Depth Priors in Visual-Inertial Initialization](https://mlsanity.com/paper/2204.09171/) (2022)
- [Revisiting visual-inertial structure from motion for odometry and SLAM initialization](https://mlsanity.com/paper/2006.06017/) (2020)
- [An Accurate Filter-based Visual Inertial External Force Estimator via Instantaneous Accelerometer Update](https://mlsanity.com/paper/2408.16354/) (2024)

## Built on by (most cited first, 2 of 2)

- [Structureless VIO](https://mlsanity.com/paper/2505.12337/) (2025)
- [Robust Structureless Monocular Visual Inertial Initialization Exploiting Line Features and Vanishing Points](https://mlsanity.com/paper/2609.21186/) (2026)

## Abstract (arXiv)

Monocular visual inertial odometry (VIO) has facilitated a wide range of real-time motion tracking applications, thanks to the small size of the sensor suite and low power consumption. To successfully bootstrap VIO algorithms, the initialization module is extremely important. Most initialization methods rely on the reconstruction of 3D visual point clouds. These methods suffer from high computational cost as state vector contains both motion states and 3D feature points. To address this issue, some researchers recently proposed a structureless initialization method, which can solve the initial state without recovering 3D structure. However, this method potentially compromises performance due to the decoupled estimation of rotation and translation, as well as linear constraints. To improve its accuracy, we propose novel structureless visual-inertial bundle adjustment to further refine previous structureless solution. Extensive experiments on real-world datasets show our method significantly improves the VIO initialization accuracy, while maintaining real-time performance.