# Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias and Rolling Shutter Effect

arXiv: https://arxiv.org/abs/1705.04300 · HTML: https://mlsanity.com/paper/1705.04300/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2017.

## Built on by (most cited first, 12 of 12)

- [Unsupervised Monocular Depth Estimation in Highly Complex Environments](https://mlsanity.com/paper/2107.13137/) (2021)
- [Efficient Deep Visual and Inertial Odometry with Adaptive Visual Modality Selection](https://mlsanity.com/paper/2205.06187/) (2022)
- [MBA-VO: Motion Blur Aware Visual Odometry](https://mlsanity.com/paper/2103.13684/) (2021)
- [OV2SLAM : A Fully Online and Versatile Visual SLAM for Real-Time Applications](https://mlsanity.com/paper/2102.04060/) (2021)
- [A Model for Multi-View Residual Covariances based on Perspective Deformation](https://mlsanity.com/paper/2202.00765/) (2022)
- [Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression](https://mlsanity.com/paper/2208.00919/) (2022)
- [Comparison of Various SLAM Systems for Mobile Robot in an Indoor Environment](https://mlsanity.com/paper/2501.09490/) (2025)
- [Discriminately Treating Motion Components Evolves Joint Depth and Ego-Motion Learning](https://mlsanity.com/paper/2511.01502/) (2025)
- [Drift Reduction for Monocular Visual Odometry of Intelligent Vehicles using Feedforward Neural Networks](https://mlsanity.com/paper/2207.00909/) (2022)
- [Motion-Bias-Free Feature-Based SLAM](https://mlsanity.com/paper/2309.06792/) (2023)
- [An Attention-Based Deep Learning Architecture for Real-Time Monocular Visual Odometry: Applications to GPS-free Drone Navigation](https://mlsanity.com/paper/2404.17745/) (2024)
- [Uncertainty-Aware Adaptive Sensor Fusion for Autonomous Navigation](https://mlsanity.com/paper/2606.05437/) (2026)

## Abstract (arXiv)

Monocular visual odometry (VO) and simultaneous localization and mapping (SLAM) have seen tremendous improvements in accuracy, robustness and efficiency, and have gained increasing popularity over recent years. Nevertheless, not so many discussions have been carried out to reveal the influences of three very influential yet easily overlooked aspects: photometric calibration, motion bias and rolling shutter effect. In this work, we evaluate these three aspects quantitatively on the state of the art of direct, feature-based and semi-direct methods, providing the community with useful practical knowledge both for better applying existing methods and developing new algorithms of VO and SLAM. Conclusions (some of which are counter-intuitive) are drawn with both technical and empirical analyses to all of our experiments. Possible improvements on existing methods are directed or proposed, such as a sub-pixel accuracy refinement of ORB-SLAM which boosts its performance.