Abstract
This research paper focuses on the problem of dynamic objects and their impact on effective motion planning and localization. The paper proposes a two-step process to address this challenge, which involves finding the dynamic objects in the scene using a Flow-based method and then using a deep Video inpainting algorithm to remove them. The study aims to test the validity of this approach by comparing it with baseline results using two state-of-the-art SLAM algorithms, ORB-SLAM2 and LSD, and understanding the impact of dynamic objects and the corresponding trade-offs. The proposed approach does not require any significant modifications to the baseline SLAM algorithms, and therefore, the computational effort required remains unchanged. The paper presents a detailed analysis of the results obtained and concludes that the proposed method is effective in removing dynamic objects from the scene, leading to improved SLAM performance.
Abstract (translated)
本研究专注于动态物体及其对有效运动规划和定位的影响问题。本文提出了一个两步骤的方法来解决这一挑战。该方法涉及使用流的方法在场景中查找动态物体,然后使用深度视频插值算法将它们删除。研究旨在测试这种方法的有效性,通过使用两个先进的SLAM算法,ORB-SLAM2和LSD,与基线结果进行比较,并理解动态物体的影响和相应的权衡。 proposed approach 不需要对基线SLAM算法进行任何重大修改,因此所需的计算 effort 保持不变。本文详细分析了所取得的结果,并得出结论,即该方法能够有效地从场景中删除动态物体,从而提高了SLAM性能。
URL
https://arxiv.org/abs/2303.10923
