SharpSLAM: 3D Object-Oriented Visual SLAM with Deblurring for Agile Drones

TL;DR

SharpSLAM enhances 3D object-oriented visual SLAM in dynamic environments by applying image deblurring, leading to improved localization, mapping, and object reconstruction accuracy for agile drones.

Contribution

The paper introduces SharpSLAM, a novel deblurring algorithm that significantly improves visual SLAM performance under high dynamic motion conditions.

Findings

Increased F-score from 82.9% to 86.2%.

Reduced RMSE of signed distance function from 17.2 to 15.4 cm.

Improved object IoU from 74.5% to 75.7%.

Abstract

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.