通过去模糊提升无人机视觉定位与建模精度
SharpSLAM: 3D Object-Oriented Visual SLAM with Deblurring for Agile Drones
- 引入图像去模糊技术降低高速运动带来的模糊影响
- 物体检测F-score提升至86.2%，定位交并比达75.7%
- 适合高速无人机、自动驾驶等实时三维重建场景
本文针对高速运动下视觉SLAM中3D重建与分割质量下降问题，提出SharpSLAM算法，通过图像去模糊提升RGB图像质量。实验表明，该方法显著改善了物体检测性能，F-score从82.9%提升至86.2%，得益于更多特征点与地图点；符号距离函数的RMSE由17.2 cm降至15.4 cm；物体定位的交并比（IoU）从74.5%提升至75.7%。SharpSLAM在提升DSP-SLAM中3D重建与分割质量方面具有显著潜力，可广泛应用于机器人、自动驾驶及增强现实等领域。
原文摘要 · Abstract (English)
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.
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