# Constrained Bundle Adjustment for Structure From Motion Using Uncalibrated Multi-Camera Systems

arXiv: https://arxiv.org/abs/2204.04145 · HTML: https://mlsanity.com/paper/2204.04145/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 21 references that are in the map.
- First posted on arXiv 8 Apr 2022.

## Builds on (most cited first, 2 of 2)

- [3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection](https://mlsanity.com/paper/1708.09839/) (2017)
- [Project AutoVision: Localization and 3D Scene Perception for an Autonomous Vehicle with a Multi-Camera System](https://mlsanity.com/paper/1809.05477/) (2018)

## Built on by (most cited first, 1 of 1)

- [Uncertainty Quantification Framework for Aerial and UAV Photogrammetry through Error Propagation](https://mlsanity.com/paper/2507.13486/) (2025)

## Abstract (arXiv)

Structure from motion using uncalibrated multi-camera systems is a challenging task. This paper proposes a bundle adjustment solution that implements a baseline constraint respecting that these cameras are static to each other. We assume these cameras are mounted on a mobile platform, uncalibrated, and coarsely synchronized. To this end, we propose the baseline constraint that is formulated for the scenario in which the cameras have overlapping views. The constraint is incorporated in the bundle adjustment solution to keep the relative motion of different cameras static. Experiments were conducted using video frames of two collocated GoPro cameras mounted on a vehicle with no system calibration. These two cameras were placed capturing overlapping contents. We performed our bundle adjustment using the proposed constraint and then produced 3D dense point clouds. Evaluations were performed by comparing these dense point clouds against LiDAR reference data. We showed that, as compared to traditional bundle adjustment, our proposed method achieved an improvement of 29.38%.