SharpSLAM：面向敏捷无人机的带去模糊功能的3D面向对象视觉SLAM
SharpSLAM: 3D Object-Oriented Visual SLAM with Deblurring for Agile Drones
AI总结：
针对敏捷无人机高动态运动导致面向对象视觉SLAM性能下降的问题，提出SharpSLAM算法，通过图像去模糊提升DSP-SLAM的三维重建与分割质量，在检测精度、距离误差及定位交并比上均有提升，可应用于多领域。
摘要：
本文聚焦于通过提升RGB图像质量来改善DSP-SLAM中三维重建与分割质量的算法。我们开发的SharpSLAM算法旨在通过图像去模糊降低高动态运动对面向对象视觉SLAM的影响，从定位、建图到物体重建，全方位提升面向对象SLAM的性能。\n实验结果表明，由于特征点及对应地图点数量增加，目标检测质量得到显著提升，F-score从82.9%提升至86.2%；有符号距离函数的RMSE也从17.2厘米降至15.4厘米。此外，我们的方案提升了物体定位精度，IoU从74.5%提升至75.7%。SharpSLAM算法有望大幅提升DSP-SLAM的三维重建与分割质量，并可应用于机器人、自动驾驶汽车及增强现实等广泛领域。
英文摘要：
The paper focuses on the algorithm for improving the quality of 3D reconstruction and segmentation in DSP-SLAM by enhancing the RGB image quality. SharpSLAM algorithm developed by us aims to decrease the influence of high dynamic motion on visual object-oriented SLAM through image deblurring, improving all aspects of object-oriented SLAM, including localization, mapping, and object reconstruction. The experimental results revealed noticeable improvement in object detection quality, with F-score increased from 82.9% to 86.2% due to the higher number of features and corresponding map points. The RMSE of signed distance function has also decreased from 17.2 to 15.4 cm. Furthermore, our solution has enhanced object positioning, with an increase in the IoU from 74.5% to 75.7%. SharpSLAM algorithm has the potential to highly improve the quality of 3D reconstruction and segmentation in DSP-SLAM and to impact a wide range of fields, including robotics, autonomous vehicles, and augmented reality.
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。
