# Direct Sparse Odometry with Rolling Shutter

arXiv: https://arxiv.org/abs/1808.00558 · HTML: https://mlsanity.com/paper/1808.00558/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2018.

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

- [DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras](https://mlsanity.com/paper/2108.10869/) (2021)
- [TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo](https://mlsanity.com/paper/2111.07418/) (2021)
- [VOLDOR: Visual Odometry from Log-logistic Dense Optical flow Residuals](https://mlsanity.com/paper/2104.06789/) (2021)
- [Revisiting Rolling Shutter Bundle Adjustment: Toward Accurate and Fast Solution](https://mlsanity.com/paper/2209.08503/) (2022)
- [Asynchronous Multi-View SLAM](https://mlsanity.com/paper/2101.06562/) (2021)
- [GeoRefine: Self-Supervised Online Depth Refinement for Accurate Dense Mapping](https://mlsanity.com/paper/2205.01656/) (2022)
- [Single-Scanline Relative Pose Estimation for Rolling Shutter Cameras](https://mlsanity.com/paper/2506.22069/) (2025)
- [Minimal Solvers for Full DoF Motion Estimation from Asynchronous Tracks](https://mlsanity.com/paper/2508.17537/) (2025)
- [Hyperion -- A fast, versatile symbolic Gaussian Belief Propagation framework for Continuous-Time SLAM](https://mlsanity.com/paper/2407.07074/) (2024)
- [Joint Spatial-Temporal Calibration for Camera and Global Pose Sensor](https://mlsanity.com/paper/2403.00976/) (2024)
- [An indoor DSO-based ceiling-vision odometry system for indoor industrial environments](https://mlsanity.com/paper/2412.02950/) (2024)
- [Rolling Shutter Camera Self-Calibration](https://mlsanity.com/paper/2608.01509/) (2026)

## Abstract (arXiv)

Neglecting the effects of rolling-shutter cameras for visual odometry (VO) severely degrades accuracy and robustness. In this paper, we propose a novel direct monocular VO method that incorporates a rolling-shutter model. Our approach extends direct sparse odometry which performs direct bundle adjustment of a set of recent keyframe poses and the depths of a sparse set of image points. We estimate the velocity at each keyframe and impose a constant-velocity prior for the optimization. In this way, we obtain a near real-time, accurate direct VO method. Our approach achieves improved results on challenging rolling-shutter sequences over state-of-the-art global-shutter VO.