# DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM

arXiv: https://arxiv.org/abs/2010.07820 · HTML: https://mlsanity.com/paper/2010.07820/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2020.

## Built on by (most cited first, 30 of 47)

- [Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs](https://mlsanity.com/paper/2101.06894/) (2021)
- [MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization](https://mlsanity.com/paper/2101.06605/) (2021)
- [DynaMem: Online Dynamic Spatio-Semantic Memory for Open World Mobile Manipulation](https://mlsanity.com/paper/2411.04999/) (2024)
- [PVO: Panoptic Visual Odometry](https://mlsanity.com/paper/2207.01610/) (2022)
- [DDN-SLAM: Real-time Dense Dynamic Neural Implicit SLAM](https://mlsanity.com/paper/2401.01545/) (2024)
- [Uncertainty-aware Unsupervised Multi-Object Tracking](https://mlsanity.com/paper/2307.15409/) (2023)
- [PG-SLAM: Photo-realistic and Geometry-aware RGB-D SLAM in Dynamic Environments](https://mlsanity.com/paper/2411.15800/) (2024)
- [DynaGSLAM: Real-Time Gaussian-Splatting SLAM for Online Rendering, Tracking, Motion Predictions of Moving Objects in Dynamic Scenes](https://mlsanity.com/paper/2503.11979/) (2025)
- [RD-VIO: Robust Visual-Inertial Odometry for Mobile Augmented Reality in Dynamic Environments](https://mlsanity.com/paper/2310.15072/) (2023)
- [Learning to Complete Object Shapes for Object-level Mapping in Dynamic Scenes](https://mlsanity.com/paper/2208.05067/) (2022)
- [MOTSLAM: MOT-assisted monocular dynamic SLAM using single-view depth estimation](https://mlsanity.com/paper/2210.02038/) (2022)
- [ObVi-SLAM: Long-Term Object-Visual SLAM](https://mlsanity.com/paper/2309.15268/) (2023)
- [Semantic Visual Simultaneous Localization and Mapping: A Survey](https://mlsanity.com/paper/2209.06428/) (2022)
- [S3LAM: Structured Scene SLAM](https://mlsanity.com/paper/2109.07339/) (2021)
- [Indoor Navigation Assistance for Visually Impaired People via Dynamic SLAM and Panoptic Segmentation with an RGB-D Sensor](https://mlsanity.com/paper/2204.01154/) (2022)
- [TwistSLAM: Constrained SLAM in Dynamic Environment](https://mlsanity.com/paper/2202.12384/) (2022)
- [Anthropomorphic Grasping with Neural Object Shape Completion](https://mlsanity.com/paper/2311.02510/) (2023)
- [RP-VIO: Robust Plane-based Visual-Inertial Odometry for Dynamic Environments](https://mlsanity.com/paper/2103.10400/) (2021)
- [OccluTrack: Rethinking Awareness of Occlusion for Enhancing Multiple Pedestrian Tracking](https://mlsanity.com/paper/2309.10360/) (2023)
- [3DS-SLAM: A 3D Object Detection based Semantic SLAM towards Dynamic Indoor Environments](https://mlsanity.com/paper/2310.06385/) (2023)
- [GSLAMOT: A Tracklet and Query Graph-based Simultaneous Locating, Mapping, and Multiple Object Tracking System](https://mlsanity.com/paper/2408.09191/) (2024)
- [Visual-Inertial Multi-Instance Dynamic SLAM with Object-level Relocalisation](https://mlsanity.com/paper/2208.04274/) (2022)
- [DynaVINS++: Robust Visual-Inertial State Estimator in Dynamic Environments by Adaptive Truncated Least Squares and Stable State Recovery](https://mlsanity.com/paper/2410.15373/) (2024)
- [TwistSLAM++: Fusing multiple modalities for accurate dynamic semantic SLAM](https://mlsanity.com/paper/2209.07888/) (2022)
- [SeMLaPS: Real-time Semantic Mapping with Latent Prior Networks and Quasi-Planar Segmentation](https://mlsanity.com/paper/2306.16585/) (2023)
- [UniQuadric: A SLAM Backend for Unknown Rigid Object 3D Tracking and Light-Weight Modeling](https://mlsanity.com/paper/2309.17036/) (2023)
- [Monocular Localization with Semantics Map for Autonomous Vehicles](https://mlsanity.com/paper/2406.03835/) (2024)
- [Neural Implicit Representation for Highly Dynamic LiDAR Mapping and Odometry](https://mlsanity.com/paper/2409.17729/) (2024)
- [CAD-SLAM: Consistency-Aware Dynamic SLAM with Dynamic-Static Decoupled Mapping](https://mlsanity.com/paper/2505.19420/) (2025)
- [ProDyG: Progressive Dynamic Scene Reconstruction via Gaussian Splatting from Monocular Videos](https://mlsanity.com/paper/2509.17864/) (2025)

## Abstract (arXiv)

The assumption of scene rigidity is common in visual SLAM algorithms. However, it limits their applicability in populated real-world environments. Furthermore, most scenarios including autonomous driving, multi-robot collaboration and augmented/virtual reality, require explicit motion information of the surroundings to help with decision making and scene understanding. We present in this paper DynaSLAM II, a visual SLAM system for stereo and RGB-D configurations that tightly integrates the multi-object tracking capability. DynaSLAM II makes use of instance semantic segmentation and of ORB features to track dynamic objects. The structure of the static scene and of the dynamic objects is optimized jointly with the trajectories of both the camera and the moving agents within a novel bundle adjustment proposal. The 3D bounding boxes of the objects are also estimated and loosely optimized within a fixed temporal window. We demonstrate that tracking dynamic objects does not only provide rich clues for scene understanding but is also beneficial for camera tracking. The project code will be released upon acceptance.