# Hybrid Visual SLAM for Underwater Vehicle Manipulator Systems

arXiv: https://arxiv.org/abs/2112.03826 · HTML: https://mlsanity.com/paper/2112.03826/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2021.

## Built on by (most cited first, 5 of 5)

- [Metrically Scaled Monocular Depth Estimation through Sparse Priors for Underwater Robots](https://mlsanity.com/paper/2310.16750/) (2023)
- [UWStereo: A Large Synthetic Dataset for Underwater Stereo Matching](https://mlsanity.com/paper/2409.01782/) (2024)
- [Refractive COLMAP: Refractive Structure-from-Motion Revisited](https://mlsanity.com/paper/2403.08640/) (2024)
- [PUGS: Perceptual Uncertainty for Grasp Selection in Underwater Environments](https://mlsanity.com/paper/2502.09824/) (2025)
- [SPADE: Sparsity Adaptive Depth Estimator for Zero-Shot, Real-Time, Monocular Depth Estimation in Underwater Environments](https://mlsanity.com/paper/2510.25463/) (2025)

## Abstract (arXiv)

This paper presents a novel visual feature based scene mapping method for underwater vehicle manipulator systems (UVMSs), with specific emphasis on robust mapping in natural seafloor environments. Our method uses GPU accelerated SIFT features in a graph optimization framework to build a feature map. The map scale is constrained by features from a vehicle mounted stereo camera, and we exploit the dynamic positioning capability of the manipulator system by fusing features from a wrist mounted fisheye camera into the map to extend it beyond the limited viewpoint of the vehicle mounted cameras. Our hybrid SLAM method is evaluated on challenging image sequences collected with a UVMS in natural deep seafloor environments of the Costa Rican continental shelf margin, and we also evaluate the stereo only mode on a shallow reef survey dataset. Results on these datasets demonstrate the high accuracy of our system and suitability for operating in diverse and natural seafloor environments. We also contribute these datasets for public use.