TURTLMap：面向低纹理水下环境的低成本无人水下航行器实时定位与稠密建图
TURTLMap: Real-time Localization and Dense Mapping of Low-texture Underwater Environments with a Low-cost Unmanned Underwater Vehicle
AI总结：
针对低纹理水下环境现有定位建图方法适用性差的问题，提出低成本的TURTLMap实时定位与稠密建图方案，经真实水箱实验验证，其在波浪条件下仍能实现精准鲁棒定位与高精度建图。
摘要：
当前水下环境定位与建图领域已有大量研究，但现有前沿方法仍受水下场景中普遍存在的低纹理环境制约，难以应用于多样的真实场景。本文提出TURTLMap，这是一款针对无纹理水下环境的新型实时定位与建图方案。该方案成本低廉，既能精准追踪机器人位姿，又能实时构建低纹理环境的稠密地图。研究人员利用搭载动作捕捉系统的室内水箱采集的真实数据，并结合真值参考地图对所提方法进行评估。定性与定量结果均验证，即便在有波浪的条件下，该系统仍能实现精准鲁棒的定位与高精度稠密建图。TURTLMap的项目页面为https://umfieldrobotics.github.io/TURTLMap。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
