# Real-time SLAM Pipeline in Dynamics Environment

arXiv: https://arxiv.org/abs/2303.02272 · HTML: https://mlsanity.com/paper/2303.02272/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 4 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction](https://mlsanity.com/paper/1604.00449/) (2016)
- [Learning Deconvolution Network for Semantic Segmentation](https://mlsanity.com/paper/1505.04366/) (2015)
- [3D Hand Pose Detection in Egocentric RGB-D Images](https://mlsanity.com/paper/1412.0065/) (2014)
- [Depth-based hand pose estimation: methods, data, and challenges](https://mlsanity.com/paper/1504.06378/) (2015)

## Abstract (arXiv)

Inspired by the recent success of application of dense data approach by using ORB-SLAM and RGB-D SLAM, we propose a better pipeline of real-time SLAM in dynamics environment. Different from previous SLAM which can only handle static scenes, we are presenting a solution which use RGB-D SLAM as well as YOLO real-time object detection to segment and remove dynamic scene and then construct static scene 3D. We gathered a dataset which allows us to jointly consider semantics, geometry, and physics and thus enables us to reconstruct the static scene while filtering out all dynamic objects.