# Dynamic Object Removal for Effective Slam

arXiv: https://arxiv.org/abs/2303.10923 · HTML: https://mlsanity.com/paper/2303.10923/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 18 references that are in the map.
- First posted on arXiv 20 Mar 2023.

## Builds on (most cited first, 12 of 12)

- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Deep Video Inpainting](https://mlsanity.com/paper/1905.01639/) (2019)
- [Coherent Semantic Attention for Image Inpainting](https://mlsanity.com/paper/1905.12384/) (2019)
- [Unsupervised Moving Object Detection via Contextual Information Separation](https://mlsanity.com/paper/1901.03360/) (2019)
- [Image Inpainting with Learnable Bidirectional Attention Maps](https://mlsanity.com/paper/1909.00968/) (2019)
- [AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation](https://mlsanity.com/paper/1803.01599/) (2018)
- [The 2018 DAVIS Challenge on Video Object Segmentation](https://mlsanity.com/paper/1803.00557/) (2018)
- [Ask, Acquire, and Attack: Data-free UAP Generation using Class Impressions](https://mlsanity.com/paper/1808.01153/) (2018)
- [Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding Manifold](https://mlsanity.com/paper/1812.02592/) (2018)
- [Faster Unsupervised Semantic Inpainting: A GAN Based Approach](https://mlsanity.com/paper/1908.04968/) (2019)

## Built on by (most cited first, 3 of 3)

- [KeyDetect --Detection of anomalies and user based on Keystroke Dynamics](https://mlsanity.com/paper/2304.03958/) (2023)
- [Learning video embedding space with Natural Language Supervision](https://mlsanity.com/paper/2303.14584/) (2023)
- [TransDocs: Optical Character Recognition with word to word translation](https://mlsanity.com/paper/2304.07637/) (2023)

## Abstract (arXiv)

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.