Dynamic Object Removal for Effective Slam
This addresses localization and motion planning issues in robotics and autonomous systems, but it is incremental as it builds on existing SLAM methods without major modifications.
The paper tackled the problem of dynamic objects degrading SLAM performance by proposing a two-step process to detect and remove them, resulting in improved performance without increasing computational cost.
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.