# TwistSLAM: Constrained SLAM in Dynamic Environment

arXiv: https://arxiv.org/abs/2202.12384 · HTML: https://mlsanity.com/paper/2202.12384/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 24 Feb 2022.

## Builds on (most cited first, 15 of 15)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping](https://mlsanity.com/paper/1910.02490/) (2019)
- [DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes](https://mlsanity.com/paper/1806.05620/) (2018)
- [CubeSLAM: Monocular 3D Object SLAM](https://mlsanity.com/paper/1806.00557/) (2018)
- [MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects](https://mlsanity.com/paper/1804.09194/) (2018)
- [DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments](https://mlsanity.com/paper/1809.08379/) (2018)
- [Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects](https://mlsanity.com/paper/1706.06629/) (2017)
- [DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM](https://mlsanity.com/paper/2010.07820/) (2020)
- [VDO-SLAM: A Visual Dynamic Object-aware SLAM System](https://mlsanity.com/paper/2005.11052/) (2020)
- [ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings](https://mlsanity.com/paper/2003.12980/) (2020)
- [S3LAM: Structured Scene SLAM](https://mlsanity.com/paper/2109.07339/) (2021)

## Built on by (most cited first, 4 of 4)

- [Embodied AI: From LLMs to World Models](https://mlsanity.com/paper/2509.20021/) (2025)
- [Semantic Visual Simultaneous Localization and Mapping: A Survey](https://mlsanity.com/paper/2209.06428/) (2022)
- [TwistSLAM++: Fusing multiple modalities for accurate dynamic semantic SLAM](https://mlsanity.com/paper/2209.07888/) (2022)
- [UniQuadric: A SLAM Backend for Unknown Rigid Object 3D Tracking and Light-Weight Modeling](https://mlsanity.com/paper/2309.17036/) (2023)

## Abstract (arXiv)

Classical visual simultaneous localization and mapping (SLAM) algorithms usually assume the environment to be rigid. This assumption limits the applicability of those algorithms as they are unable to accurately estimate the camera poses and world structure in real life scenes containing moving objects (e.g. cars, bikes, pedestrians, etc.). To tackle this issue, we propose TwistSLAM: a semantic, dynamic and stereo SLAM system that can track dynamic objects in the environment. Our algorithm creates clusters of points according to their semantic class. Thanks to the definition of inter-cluster constraints modeled by mechanical joints (function of the semantic class), a novel constrained bundle adjustment is then able to jointly estimate both poses and velocities of moving objects along with the classical world structure and camera trajectory. We evaluate our approach on several sequences from the public KITTI dataset and demonstrate quantitatively that it improves camera and object tracking compared to state-of-the-art approaches.