# TURTLMap: Real-time Localization and Dense Mapping of Low-texture Underwater Environments with a Low-cost Unmanned Underwater Vehicle

arXiv: https://arxiv.org/abs/2408.01569 · HTML: https://mlsanity.com/paper/2408.01569/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2024.

## Built on by (most cited first, 7 of 7)

- [OceanSim: A GPU-Accelerated Underwater Robot Perception Simulation Framework](https://mlsanity.com/paper/2503.01074/) (2025)
- [Image-Based Relocalization and Alignment for Long-Term Monitoring of Dynamic Underwater Environments](https://mlsanity.com/paper/2503.04096/) (2025)
- [PUGS: Perceptual Uncertainty for Grasp Selection in Underwater Environments](https://mlsanity.com/paper/2502.09824/) (2025)
- [Infinite Leagues Under the Sea: Photorealistic 3D Underwater Terrain Generation by Latent Fractal Diffusion Models](https://mlsanity.com/paper/2503.06784/) (2025)
- [FishDetector-R1: Unified MLLM-Based Framework with Reinforcement Fine-Tuning for Weakly Supervised Fish Detection, Segmentation, and Counting](https://mlsanity.com/paper/2512.05996/) (2025)
- [Long-Term Multi-Session 3D Reconstruction Under Substantial Appearance Change](https://mlsanity.com/paper/2602.20584/) (2026)
- [ReefMapGS: Enabling Large-Scale Underwater Reconstruction by Closing the Loop Between Multimodal SLAM and Gaussian Splatting](https://mlsanity.com/paper/2604.11992/) (2026)

## Abstract (arXiv)

Significant work has been done on advancing localization and mapping in underwater environments. Still, state-of-the-art methods are challenged by low-texture environments, which is common for underwater settings. This makes it difficult to use existing methods in diverse, real-world scenes. In this paper, we present TURTLMap, a novel solution that focuses on textureless underwater environments through a real-time localization and mapping method. We show that this method is low-cost, and capable of tracking the robot accurately, while constructing a dense map of a low-textured environment in real-time. We evaluate the proposed method using real-world data collected in an indoor water tank with a motion capture system and ground truth reference map. Qualitative and quantitative results validate the proposed system achieves accurate and robust localization and precise dense mapping, even when subject to wave conditions. The project page for TURTLMap is https://umfieldrobotics.github.io/TURTLMap.