# DOT: Dynamic Object Tracking for Visual SLAM

arXiv: https://arxiv.org/abs/2010.00052 · HTML: https://mlsanity.com/paper/2010.00052/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2020.

## Built on by (most cited first, 16 of 16)

- [Robust Dynamic Radiance Fields](https://mlsanity.com/paper/2301.02239/) (2023)
- [ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wild](https://mlsanity.com/paper/2207.09137/) (2022)
- [DeepFusionMOT: A 3D Multi-Object Tracking Framework Based on Camera-LiDAR Fusion with Deep Association](https://mlsanity.com/paper/2202.12100/) (2022)
- [ObVi-SLAM: Long-Term Object-Visual SLAM](https://mlsanity.com/paper/2309.15268/) (2023)
- [DirectTracker: 3D Multi-Object Tracking Using Direct Image Alignment and Photometric Bundle Adjustment](https://mlsanity.com/paper/2209.14965/) (2022)
- [ViewBirdiformer: Learning to recover ground-plane crowd trajectories and ego-motion from a single ego-centric view](https://mlsanity.com/paper/2210.06332/) (2022)
- [Self-Improving SLAM in Dynamic Environments: Learning When to Mask](https://mlsanity.com/paper/2210.08350/) (2022)
- [UniQuadric: A SLAM Backend for Unknown Rigid Object 3D Tracking and Light-Weight Modeling](https://mlsanity.com/paper/2309.17036/) (2023)
- [Jacobian Computation for Cumulative B-Splines on SE(3) and Application to Continuous-Time Object Tracking](https://mlsanity.com/paper/2201.10602/) (2022)
- [DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization](https://mlsanity.com/paper/2202.11431/) (2022)
- [HabitatDyn Dataset: Dynamic Object Detection to Kinematics Estimation](https://mlsanity.com/paper/2304.10854/) (2023)
- [Motion-Bias-Free Feature-Based SLAM](https://mlsanity.com/paper/2309.06792/) (2023)
- [SPORTS: Simultaneous Panoptic Odometry, Rendering, Tracking and Segmentation for Urban Scenes Understanding](https://mlsanity.com/paper/2510.12749/) (2025)
- [DefVINS: Visual-Inertial Odometry for Deformable Scenes](https://mlsanity.com/paper/2601.00702/) (2026)
- [End-to-End Visual Odometry with RNNs and Attention](https://mlsanity.com/paper/2609.26188/) (2026)
- [Know-Your-Scene (KYS)-SLAM: Hierarchical Semantic-Motion Priors for Feature Matching in Stereo Visual SLAM](https://mlsanity.com/paper/2609.27509/) (2026)

## Abstract (arXiv)

In this paper we present DOT (Dynamic Object Tracking), a front-end that added to existing SLAM systems can significantly improve their robustness and accuracy in highly dynamic environments. DOT combines instance segmentation and multi-view geometry to generate masks for dynamic objects in order to allow SLAM systems based on rigid scene models to avoid such image areas in their optimizations. To determine which objects are actually moving, DOT segments first instances of potentially dynamic objects and then, with the estimated camera motion, tracks such objects by minimizing the photometric reprojection error. This short-term tracking improves the accuracy of the segmentation with respect to other approaches. In the end, only actually dynamic masks are generated. We have evaluated DOT with ORB-SLAM 2 in three public datasets. Our results show that our approach improves significantly the accuracy and robustness of ORB-SLAM 2, especially in highly dynamic scenes.