Awesome_Dynamic_SLAM

The following papers focus on SLAM in dynamic environments and life-long SLAM. In dynamic environments, there are two kinds of robust SLAM: first is detection & removal, and the second is detection & tracking. Although mapping in dynamic environments is not my focus, I will also include some interesting articles.

Vision indicates the pipeline is built with a camera. Others are the same, such as lidar, radar, sensor fusion.

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2025–2026 Updates

Last updated: August 2026. Entries below link to the paper and the authors’ project or code repository when available.

Dynamic SLAM and 4D Mapping

• (CVPR 2026) Dynamic Visual SLAM using a General 3D Prior — monocular SLAM using feed-forward 3D priors for motion segmentation and depth; repository and demo.
• (CVPR 2026) DROID-SLAM in the Wild — real-time RGB SLAM using uncertainty-aware bundle adjustment for unknown dynamic objects and cluttered scenes; code and dataset.
• (CVPR 2026 Highlight) Flow4DGS-SLAM: Optical Flow-Guided 4D Gaussian Splatting SLAM — category-agnostic motion decomposition and efficient dynamic reconstruction; code.
• (CVPR Findings 2026) RU4D-SLAM: Reweighting Uncertainty in Gaussian Splatting SLAM for 4D Scene Reconstruction — uncertainty-aware tracking and dynamic 4D mapping; code.
• (WACV 2026) DynaGSLAM: Real-Time Gaussian-Splatting SLAM for Online Rendering, Tracking, Motion Predictions of Moving Objects — jointly estimates ego motion and models moving objects; code.
• (IEEE RA-L 2026) DOGL-SLAM: Dynamic Object-Level SLAM via Joint Gaussian-Landmark Tracking — object-level tracking with a Gaussian map; code and evaluation.
• (ECCV NeuSLAM Workshop 2026) QUORUM: Multi-View Feature Consensus for Open-Vocabulary SLAM in Dynamic Scenes — monocular semantic SLAM with temporal feature consensus for separating static surfaces from moving objects; code.
• (arXiv 2026) DynoSLAM: Dynamic SLAM with Generative Graph Neural Networks for Real-World Social Navigation — integrates stochastic socially-aware motion priors into GraphSLAM; code and simulator.
• (arXiv 2026) A Stereo Visual SLAM System Using Object-Level Motion Estimation and Geometric Filtering Based on Cross Disparity — OCD-SLAM combines stereo geometric filtering, 3D object detection, and tracking.
• (arXiv 2026) LST-SLAM: A Stereo Thermal SLAM System for Kilometer-Scale Dynamic Environments — thermal feature learning, dynamic feature suppression, loop closure, and global optimization.
• (IROS 2026) PLED-VINS: A Point-Line Event-Based Visual Inertial SLAM for Dynamic Environments — combines temporal and geometric reliability to suppress moving-object observations from an event camera.
• (ICRA 2026) GGD-SLAM: Monocular 3DGS SLAM Powered by Generalizable Motion Model for Dynamic Environments — semantic-agnostic motion modeling for robust tracking and dense reconstruction without depth input.
• (Pattern Recognition 2026) RGD-SLAM: Robust Gaussian Splatting SLAM for Dynamic Environments — adaptive motion weighting and visibility-aware keyframing for RGB-D tracking and static reconstruction; code.
• (arXiv 2026) DL-SLAM: Enabling High-Fidelity Gaussian Splatting SLAM in Dynamic Environments based on Dual-Level Probability — fuses pixel- and object-level motion probabilities to retain useful constraints while removing dynamic Gaussians.
• (arXiv 2026) DAGS-SLAM: Dynamic-Aware 3DGS SLAM via Spatiotemporal Motion Probability and Uncertainty-Aware Scheduling — maintains motion probability per Gaussian and invokes semantic processing only when uncertainty requires it.
• (arXiv 2026) RADIO-ViPE: Online Tightly Coupled Multi-Modal Fusion for Open-Vocabulary Semantic SLAM in Dynamic Environments — tightly couples monocular geometry, vision, and language while handling moving and rearranged objects; code.
• (arXiv 2026) Dream-SLAM: Dreaming the Unseen for Active SLAM in Dynamic Environments — uses predicted cross-temporal views and structures for mapping and long-horizon exploration.
• (RSS Workshop 2026) MoPe: Motion Permanence for Robust Monocular Gaussian Mapping in Dynamic Environments — propagates historical motion probabilities to prevent paused or reappearing objects from contaminating the map.
• (IEEE T-RO 2025) DynoSAM: Open-Source Smoothing and Mapping Framework for Dynamic SLAM — factor-graph formulations for camera, object motion, and structure estimation; ROS 2 code.
• (IEEE RA-L 2026) Online Dynamic SLAM with Incremental Smoothing and Mapping — incremental optimization for online dynamic SLAM; implemented in DynoSAM.
• (CVPR 2025) WildGS-SLAM: Monocular Gaussian Splatting SLAM in Dynamic Environments — uncertainty-guided removal of dynamic distractors without predefined object categories; code.
• (ICCV 2025) DyGS-SLAM: Real-Time Accurate Localization and Gaussian Reconstruction for Dynamic Scenes — dynamic feature detection, corrected object masks, and adaptive feature densification.
• (IROS 2025) Embracing Dynamics: Dynamics-Aware 4D Gaussian Splatting SLAM — represents temporal changes directly with 4D Gaussians; the D4DGS-SLAM project repository releases its differentiable 4D Gaussian rasterizer.
• (ICRA 2025) Dy3DGS-SLAM: Monocular 3D Gaussian Splatting SLAM for Dynamic Environments — combines optical-flow and depth masks for monocular dynamic tracking and rendering.
• (arXiv 2025/2026 revision) CAD-SLAM: Consistency-Aware Dynamic SLAM with Dynamic-Static Decoupled Mapping — detects category-agnostic motion from cross-view and cross-time inconsistencies and models dynamic content with temporal Gaussians.
• (ICME 2025) DyPho-SLAM: Real-Time Photorealistic SLAM in Dynamic Environments — resource-efficient mask refinement and adaptive feature extraction for tracking and mapping.
• (arXiv 2025) D2GSLAM: 4D Dynamic Gaussian Splatting SLAM — combines static 3D Gaussians and dynamic 4D Gaussians for joint reconstruction and camera tracking.
• (PRCV 2025) GeneA-SLAM2: Dynamic SLAM with AutoEncoder-Preprocessed Genetic Keypoints Resampling and Depth Variance-Guided Dynamic Region Removal — real-time RGB-D SLAM without a GPU; code and dataset are available.
• (IEEE TCSVT 2025) Semantic-Independent Dynamic SLAM Based on Geometric Re-Clustering and Optical Flow Residuals — handles unknown dynamic categories using geometric and motion cues; code and RGB-D sequences.
• (CVPR 2025) 4DTAM: Non-Rigid Tracking and Mapping via Dynamic Surface Gaussians — joint camera tracking and non-rigid 4D reconstruction from RGB-D; code.
• (ICCV 2025) 4D Gaussian Splatting SLAM — incrementally tracks camera poses and reconstructs static and dynamic Gaussian radiance fields; code.
• (IROS 2025) NGD-SLAM: Towards Real-Time Dynamic SLAM without GPU — CPU-only dynamic visual SLAM using mask propagation and hybrid feature/flow tracking; code.
• (IROS 2025) Dynamic-LIO: LiDAR-Inertial Odometry in Dynamic Driving Scenarios using Label Consistency Detection — dynamic-aware LiDAR-inertial odometry with loop closure; code.
• (IEEE T-RO 2025) PG-SLAM: Photo-realistic and Geometry-aware RGB-D SLAM in Dynamic Environments — jointly maps rigid and non-rigid foreground objects, reconstructs the static background, and localizes the camera.
• (IEEE RA-L 2025) SDD-SLAM: Semantic-Driven Dynamic SLAM With Gaussian Splatting — handles both actively and passively dynamic objects through semantic Gaussians and object-level density control.
• (IEEE RA-L 2025) DQO-MAP: Real-Time Object-Level SLAM via Dual Quadrics and Gaussians — jointly estimates object poses and reconstructs multiple object shapes online; code and datasets.
• (IEEE T-MM 2025) CAD-Mesher: A Convenient, Accurate, Dense Mesh-based Mapping Module in SLAM for Dynamic Environments — a LiDAR-odometry-compatible module for clean static mesh mapping; code.
• (ICRA 2025) GARAD-SLAM: 3D Gaussian Splatting for Real-Time Anti Dynamic SLAM — maps Gaussian-level dynamic labels back to tracking and penalizes dynamic Gaussians during mapping; release repository (source pending).
• (ICRA 2025) Gassidy: Gaussian Splatting SLAM in Dynamic Environments — detects disturbances by analyzing photometric-geometric rendering-loss flows.
• (ICRA 2025) DVN-SLAM: Dynamic Visual Neural SLAM Based on Local-Global Encoding — real-time neural implicit mapping with fused global structure, local detail, and dynamic-scene robustness.
• (ICRA 2025) JPG-SLAM: Joint Point-Gaussian Splatting Representation for Dense Dynamic SLAM — combines point-based tracking with Gaussian dense scene representation.
• (ACM MM 2025) SLAM-X: Generalizable Dynamic Removal for NeRF and Gaussian Splatting SLAM — a plug-and-play zero-shot segmentation and optical-flow module for multiple dense SLAM frameworks; release repository (source pending).
• (arXiv 2025) LVD-GS: Gaussian Splatting SLAM for Dynamic Scenes via Hierarchical Explicit-Implicit Representation Collaboration Rendering — LiDAR-visual dynamic masking and scale-stable outdoor reconstruction; partial code release.
• (arXiv 2025) UP-SLAM: Adaptively Structured Gaussian SLAM with Uncertainty Prediction in Dynamic Environments — training-free open-set motion uncertainty with parallel tracking and mapping; project page.
• (arXiv 2025) VAR-SLAM: Visual Adaptive and Robust SLAM for Dynamic Environments — combines semantic filtering for known movers with an online adaptive robust loss for unknown ones; release repository (source pending).
• (arXiv 2025) IL-SLAM: Intelligent Line-assisted SLAM Based on Feature Awareness for Dynamic Environments — introduces line features only when dynamic filtering leaves too few reliable point features.
• (arXiv 2025) SR-SLAM: Scene-reliability Based RGB-D SLAM in Diverse Environments — adapts dynamic-region filtering, pose refinement, and keyframe selection to estimated scene reliability.
• (arXiv 2025) IDY-VINS: A Visual-Inertial Motion Prior SLAM for Dynamic Environments — uses inertial motion priors and adaptive bundle-adjustment residuals to reject dynamic landmarks.
• (RCAR 2025) Adaptive Prior Scene-Object SLAM for Dynamic Environments — assesses frame and scene reliability and reuses trusted frames to correct pose drift.
• (arXiv 2025) STAMICS: Splat, Track And Map with Integrated Consistency and Semantics for Dense RGB-D SLAM — combines Gaussian reconstruction, temporally consistent graph clustering, and open-vocabulary semantics.
• (arXiv 2025) DYNEMO-SLAM: Dynamic Entity and Motion-Aware 3D Scene Graph SLAM — jointly optimizes robot motion, dynamic entity poses, and scene structure rather than treating all movers as outliers.

Lifelong Mapping and Localization

• (ICRA 2026) Lifelong Localization in Dynamic Indoor Environments Combining Odometry with Sparse Distance Sampling — provably convergent localization using odometry and only a small number of range samples, with learned handling of environmental change.
• (arXiv 2026) Change-Robust Online Spatial-Semantic Topological Mapping — a bounded multi-hypothesis topological representation for lighting changes, furniture rearrangement, loop closures, and kidnapped-robot recovery.
• (ICRA 2025) ELite: Ephemerality Meets LiDAR-Based Lifelong Mapping — multi-session alignment, dynamic-object removal, and map updating using two-timescale ephemerality; code.
• (IEEE T-IM 2025) LL-Localizer: A Lifelong Localization System Based on Dynamic i-Octree — incremental map loading and updating across changed and unmapped areas; simplified demo.

Dynamic Object Detection and Tracking

• (2025) LV-DOT: LiDAR-Visual Dynamic Obstacle Detection and Tracking for Autonomous Robot Navigation — lightweight camera/LiDAR fusion for onboard detection and tracking; ROS code.

Related Robust Visual Odometry

• (CVPR 2026) OpenVO: Open-World Visual Odometry with Temporal Dynamics Awareness — open-world temporal reasoning for robust odometry in scenes containing unknown motion; code.
• (IEEE RA-L 2026) MVOFormer: Flow-Semantic Transformer for Robust Monocular Visual Odometry — combines dense motion and semantic cues to suppress dynamic distractors with zero-shot cross-domain generalization; code.
• (CVPR 2025) MegaSaM: Accurate, Fast and Robust Structure and Motion from Casual Dynamic Videos — estimates camera trajectories and consistent depth from dynamic, uncalibrated videos; code.

Datasets and Benchmarks

• (CVPR 2025) Wild-SLAM — dynamic monocular/RGB-D sequences with mocap ground truth plus in-the-wild iPhone sequences; evaluation and download scripts are provided with WildGS-SLAM.
• (CVPR 2026) DROID-W Dataset — seven casually captured outdoor sequences with dynamic objects, clutter, RGB frames, and LiDAR-derived ground-truth trajectories; evaluation code.
• (CVPR 2025) Sim4D — synthetic non-rigid 4D-SLAM benchmark with camera poses, time-varying geometry, color, and rendering scripts.
• (IROS 2025) M3DGR — multi-sensor ground-robot benchmark with dynamic people and systematically induced visual, LiDAR, wheel, and GNSS degradation; evaluates more than 40 SLAM systems.
• (ICRA 2025) M3DSS — multi-platform and multi-sensor SLAM dataset with event cameras, accurate ground truth, and dynamic urban driving sequences.
• (2025) SLAM&Render — 40 synchronized RGB-D, IMU, kinematic, and pose sequences with lighting changes, occlusions, and object rearrangements; benchmark repository.
• (CVPR 2026) Ghost-FWL — 24K mobile full-waveform LiDAR frames with billions of peak-level labels for ghost detection and removal; dataset and code.
• (2026) MotionScape — more than 30 hours of 4K UAV video with 6-DoF trajectories and text descriptions under highly dynamic camera motion; benchmark repository (partial public release).
• (2026) HERCULES — open-source heterogeneous UAV/UGV simulation and a kilometer-scale SLAM benchmark across dynamic desert, forest, and city environments; project page.
• (ICRA 2025) DiTer++ — multi-robot, multi-modal, multi-session SLAM data across diverse outdoor terrain; dataset and tools.
• (IEEE T-RO 2025) ROVER — a multi-season outdoor visual SLAM benchmark covering illumination, weather, vegetation, and structural changes; dataset and evaluation code.

Related survey papers:

• A survey: which features are required for dynamic visual simultaneous localization and mapping?. Zewen Xu, CAS. 2021

• State of the Art in Real-time Registration of RGB-D Images. Stotko, Patrick. University of Bonn. 2016
• Visual SLAM and Structure from Motion in Dynamic Environments: A Survey. University of Oxford. 2018
• State of the Art on 3D Reconstruction with RGB-D Cameras. Michael Zollhöfer. Stanford University. 2018

Dynamic Object Detection and Removal

• “Efficient Dynamic LiDAR Odometry for Mobile Robots with Structured Point Clouds” by Lichtenfeld, Daun, von Stryk (IROS 2024). paper and Github

• https://github.com/KTH-RPL/DynamicMap_Benchmark
  • benchmark
• Dynablox: Real-time Detection of Diverse Dynamic Objects in Complex Environments
  • Extension of Voxlobx

• (IROS 2022) CFP-SLAM: A Real-time Visual SLAM Based on Coarse-to-Fine Probability in Dynamic Environments

• (IROS 2022) DRG-SLAM: A Semantic RGB-D SLAM using Geometric Features for Indoor Dynamic Scene

• (IEEE RA-L’22) DynaVINS: A Visual-Inertial SLAM for Dynamic Environments, code: https://github.com/url-kaist/dynaVINS
  • Non-deep learning approach, using constraints to remove feature points on moving objects
• DeFlowSLAM: Self-Supervised Scene Motion Decomposition for Dynamic Dense SLAM
  • Zhejiang University, seems to be a part of DROID-SLAM, divides optical flow into dynamic and static parts for subsequent operations. The results look promising. page, code
• Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation
  • Haomo.AI, code, dynamic detection
• [2203.03923] ROLL: Long-Term Robust LiDAR-based Localization With Temporary Mapping in Changing Environments IROS 2022
  • code: https://github.com/HaisenbergPeng/ROLL
• POCD: Probabilistic Object-Level Change Detection and Volumetric Mapping in Semi-Static Scenes
  • RSS 2022, map updating in semi-static scenes
• J. Schauer and A. Nuchter, “The Peopleremover—Removing Dynamic Objects From 3-D Point Cloud Data by Traversing a Voxel Occupancy Grid,” IEEE Robot. Autom. Lett., vol. 3, no. 3, pp. 1679–1686, Jul. 2018, doi: 10.1109/LRA.2018.2801797.
  • Method for removing dynamic objects based on voxel traversal. Despite its many shortcomings, the paper proposes many tricks to solve these problems, and the results look quite good.
  • code, video
• N. Rufus, U. K. R. Nair, A. V. S. S. B. Kumar, V. Madiraju, and K. M. Krishna, “SROM: Simple Real-time Odometry and Mapping using LiDAR data for Autonomous Vehicles,” IV 2020
  • Roughly removes possible moving objects, removes the ground, and then extracts the remaining parts
• M. Schorghuber, D. Steininger, Y. Cabon, M. Humenberger, and M. Gelautz, “SLAMANTIC - Leveraging Semantics to Improve VSLAM in Dynamic Environments” ICCV 2019 workshop
  • Visual SLAM in dynamic environments. Uses semantics to calculate confidence in points, uses high-confidence points to assist low-confidence points, and ultimately determines which parts are used for localization and mapping.
• S. Gu, S. Yao, J. Yang, and H. Kong, “Semantics-Guided Moving Object Segmentation with 3D LiDAR,” arxiv 2022.05
  • Dynamic object segmentation network, based on the idea of rangenet.
• Y. Pan, B. Gao, J. Mei, S. Geng, C. Li, and H. Zhao, “SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances,” IV 2020
  • Outdoor dataset of dynamic objects, Peking University, website
• S. Pagad, D. Agarwal, S. Narayanan, K. Rangan, H. Kim, and G. Yalla, “Robust Method for Removing Dynamic Objects from Point Clouds,” ICRA 2020
  • video, dynamic removal
• L. Sun, Z. Yan, A. Zaganidis, C. Zhao, and T. Duckett, “Recurrent-OctoMap: Learning State-Based Map Refinement for Long-Term Semantic Mapping With 3-D-Lidar Data,” RAL
  • Life-long SLAM
• P. Egger, P. V. K. Borges, G. Catt, A. Pfrunder, R. Siegwart, and R. Dubé, “PoseMap: Lifelong, Multi-Environment 3D LiDAR Localization,” IROS 2018
  • Lifelong SLAM, ETH SAL group
• DynamicFilter: an Online Dynamic Objects Removal Framework for Highly Dynamic Environments, ICRA 2022
  • IJRR experts, unfortunately not open source, HKUST, SUSTech
• X. Ma, Y. Wang, B. Zhang, H.-J. Ma, and C. Luo, “DynPL-SVO: A New Method Using Point and Line Features for Stereo Visual Odometry in Dynamic Scenes.” arXiv, May 17, 2022
  • Stereo visual odometry using point and line features in dynamic scenes, Northeast University, not yet open source
• M. T. Lázaro, R. Capobianco, and G. Grisetti, “Efficient Long-term Mapping in Dynamic Environments,” IROS 2018
  • Efficient ICP scheme, achieving map entity merging. As it deals with 2D maps, there are not many things to handle. Dynamic point clouds can be removed using point visualization.
  • code,

• T. Krajník, J. P. Fentanes, J. M. Santos, and T. Duckett, “FreMEn: Frequency Map Enhancement for Long-Term Mobile Robot Autonomy in Changing Environments,” TRO 2017

• G. Kurz, M. Holoch, and P. Biber, “Geometry-based Graph Pruning for Lifelong SLAM.” IROS 2021
  • We propose a new method that considers geometric criteria for selecting vertices to prune. This is efficient, easy to implement, and results in a graph with uniformly distributed vertices that remain part of the robot trajectory. Additionally, we propose a new marginalization method that is more robust to erroneous loop closures compared to existing methods. Mainly involves optimization of the SLAM back-end, addressing how to prune the factor graph when the map or factor graph is updated.
• Quei-An Chen and Akihiro Tsukada, “Flow Supervised Neural Radiance Fields for Static-Dynamic Decomposition,” ICRA 2022
  • AI Korea, code, dynamic object removal and repair using NeRF + optical flow, video.
• W. Ding, S. Hou, H. Gao, G. Wan, and S. Song, “LiDAR Inertial Odometry Aided Robust LiDAR Localization System in Changing City Scenes,” ICRA 2020
  • Baidu’s solution using LiDAR and IMU for localization in dynamic scenes, updating the map with new elements in the scene.
  • Life-long SLAM
• G. D. Tipaldi, D. Meyer-Delius, and W. Burgard, “Lifelong localization in changing environments,” IJRR 2013
  • Life-long localization
• S. Zhu, X. Zhang, S. Guo, J. Li, and H. Liu, “Lifelong Localization in Semi-Dynamic Environment,” ICRA 2021
  • Tsinghua University, life-long localization
• F. Pomerleau, P. Krüsi, F. Colas, P. Furgale, and R. Siegwart, “Long-term 3D map maintenance in dynamic environments,” ICRA 2014
  • Map updating in dynamic environments
• D. J. Yoon, T. Y. Tang, and T. D. Barfoot, “Mapless Online Detection of Dynamic Objects in 3D Lidar.” Conference on Computer and Robot Vision (CRV) 2019
  • Point cloud dynamic detection

• Dynamic-SLAM: Semantic monocular visual localization and mapping based on deep learning in dynamic environment. Robotics and Autonomous Systems 2019

• M. Zhao et al., “A General Framework for Lifelong Localization and Mapping in Changing Environment,” IROS 2021
  • Highseer Robotics’ life-long localization paper
  • Multi-session map representation and an efficient online map update strategy, subsystems: local laser odometry (LLO), global laser matching (GLM), and pose graph optimization (PGR). LLO constructs a series of locally consistent sub-maps, GLM calculates relative constraints between incoming scan clouds and global sub-maps, and PGR collects sub-maps and constraints from LLO and GLM, prunes old sub-maps in historical maps, and performs pose graph sparsification and optimization.
• D. Henning, T. Laidlow, and S. Leutenegger, “BodySLAM: Joint Camera Localisation, Mapping, and Human Motion Tracking,” *arXiv:2205.02301
  • Combines human body reconstruction with SLAM, similar to AirDOS
• Pfreundschuh, Patrick, et al. “Dynamic Object Aware LiDAR SLAM Based on Automatic Generation of Training Data.” (ICRA 2021)
  • ETH ASL, code, video, dataset, LiDAR
  • Authors use a deep-learning approach (3D-MiniNet network) for real-time 3D dynamic object detection, filtering out dynamic objects and feeding the remaining point cloud to LOAM for conventional LiDAR SLAM. The learning method is unsupervised.
• Canovas Bruce, et al. “Speed and Memory Efficient Dense RGB-D SLAM in Dynamic Scenes.” (IROS 2020)
  • GIPSA-lab, code, video
• Yuan Xun and Chen Song, “SaD-SLAM: A Visual SLAM Based on Semantic and Depth Information,” (IROS 2020)
  • USTC, code, video
• Dong, Erqun, et al. “Pair-Navi: Peer-to-Peer Indoor Navigation with Mobile Visual SLAM,” (ICCC 2019)
  • Tsinghua, code, video, slides.

• Ji Tete, et al. “Towards Real-Time Semantic RGB-D SLAM in Dynamic Environments,” (ICRA 2021)

• Palazzolo Emanuele, et al. “ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals,” (IROS 2019)
  • code, video, University of Bonn.

• Arora Mehul, et al. “Mapping the Static Parts of Dynamic Scenes from 3D LiDAR Point Clouds Exploiting Ground Segmentation.”

• Chen Xieyuanli, et al. “Moving Object Segmentation in 3D LiDAR Data: A Learning-Based Approach Exploiting Sequential Data,” IEEE Robotics and Automation Letters, 2021
  • code, video, University of Bonn.
• Zhang Tianwei, et al. “FlowFusion: Dynamic Dense RGB-D SLAM Based on Optical Flow,” (ICRA 2020)
  • code, video.
• Zhang Tianwei, et al. “AcousticFusion: Fusing Sound Source Localization to Visual SLAM in Dynamic Environments,” IROS 2021
  • video. Combines sound signals

• Liu Yubao and Miura Jun, “RDS-SLAM: Real-Time Dynamic SLAM Using Semantic Segmentation Methods,” IEEE Access 2021

  • Liu Yubao and Miura Jun, “RDMO-SLAM: Real-Time Visual SLAM for Dynamic Environments Using Semantic Label Prediction With Optical Flow,” IEEE Access, vol. 9, 2021, pp. 106981–97. IEEE Xplore.

  • code, video.

• Cheng Jiyu, et al. “Improving Visual Localization Accuracy in Dynamic Environments Based on Dynamic Region Removal,” IEEE Transactions on Automation Science and Engineering, vol. 17, no. 3, July 2020, pp. 1585–96. IEEE Xplore.

• Soares João Carlos Virgolino, et al

. “Crowd-SLAM: Visual SLAM Towards Crowded Environments Using Object Detection,” Journal of Intelligent & Robotic Systems 2021

• code, video

• Visual Localization and Mapping in Dynamic and Changing Environments (2022), previously based on ORB-SLAM2, the latest version is based on ORB-SLAM3.

• Kaveti Pushyami and Singh Hanumant, “A Light Field Front-End for Robust SLAM in Dynamic Environments.”

• Kuen-Han Lin and Chieh-Chih Wang, “Stereo-Based Simultaneous Localization, Mapping and Moving Object Tracking,” IROS 2010

• Fu, H.; Xue, H.; Hu, X.; Liu, B., “LiDAR Data Enrichment by Fusing Spatial and Temporal Adjacent Frames,” Remote Sens. 2021, 13, 3640.

• Qian, Chenglong, et al., “RF-LIO: Removal-First Tightly-Coupled Lidar Inertial Odometry in High Dynamic Environments,” IROS 2021, XJTU

• K. Minoda, F. Schilling, V. Wüest, D. Floreano, and T. Yairi, “VIODE: A Simulated Dataset to Address the Challenges of Visual-Inertial Odometry in Dynamic Environments,” RAL 2021
  • Dynamic environment dataset, including static and dynamic levels of scenes, suitable for verification.
  • University of Tokyo, code
• W. Dai, Y. Zhang, P. Li, Z. Fang, and S. Scherer, “RGB-D SLAM in Dynamic Environments Using Point Correlations,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–1, 2020
  • Zhejiang University, using point correlations for removal.
• C. Huang, H. Lin, H. Lin, H. Liu, Z. Gao, and L. Huang, “YO-VIO: Robust Multi-Sensor Semantic Fusion Localization in Dynamic Indoor Environments,” in 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2021.
  • Uses YOLO and optical flow to identify moving objects, removes feature points for localization
  • Combines VIO
• (IROS 2022) Dynamic-VINS: RGB-D Inertial Odometry for a Resource-restricted Robot in Dynamic Environments.
  • Segmentation + motion point confidence, code, video

Dynamic Object Detection and Tracking

• Youngjae Min, Do-Un Kim, and Han-Lim Choi, “Kernel-Based 3-D Dynamic Occupancy Mapping with Particle Tracking,” 2021 IEEE International Conference on Robotics and Automation (ICRA)
  • code: https://github.com/youngjae-min/k3dom
• DyOb-SLAM: Dynamic Object Tracking SLAM System (2022)
  • Combination of VDO-SLAM and DynaSLAM

• DynaVIG: Monocular Vision/INS/GNSS Integrated Navigation and Object Tracking for AGV in Dynamic Scenes (2022)

• DirectTracker: 3D Multi-Object Tracking Using Direct Image Alignment and Photometric Bundle Adjustment (2022)
  • Direct method for dynamic object tracking, page
• (IROS 2022) MOTSLAM: MOT-assisted monocular dynamic SLAM using single-view depth estimation (2022)
• TwistSLAM++: Fusing multiple modalities for accurate dynamic semantic SLAM (2022)
  • SLAMMOT
• (IROS 2022) Visual-Inertial Multi-Instance Dynamic SLAM with Object-level Relocalisation (2022)
  • IROS 2022, lab website: https://mlr.in.tum.de/research/semanicobjectlevelanddynamicslam
• Learning to Complete Object Shapes for Object-level Mapping in Dynamic Scenes (2022), by the same author as above,
  • Based on MID-Fusion.
• T. Ma and Y. Ou, “MLO: Multi-Object Tracking and Lidar Odometry in Dynamic Environment,” arXiv, Apr. 29, 2022
  • SLAM + MOT
• Z. Wang, W. Li, Y. Shen, and B. Cai, “4-D SLAM: An Efficient Dynamic Bayes Network-Based Approach for Dynamic Scene Understanding,” IEEE Access
  • Semantic recognition of dynamics, uses UKF for dynamic tracking, but the graph results are poor.
• T. Ma and Y. Ou, “MLO: Multi-Object Tracking and Lidar Odometry in Dynamic Environment,” ArXiv 2022
  • Based on LOAM for target tracking, separately estimates moving objects and self, then fuses the results. Seems loosely coupled.
• (IROS 2022) R. Long, C. Rauch, T. Zhang, V. Ivan, T. L. Lam, and S. Vijayakumar, “RGB-D SLAM in Indoor Planar Environments with Multiple Large Dynamic Objects,”
  • Performs dynamic removal first, followed by dynamic tracking. SLAM + MOT in structured environments (surfaces)
• Qiu Yuheng, et al., “**AirDOS: Dynamic SLAM benefits from Articulated Objects,” 2021 (Arxiv)
  • code(TBA), Paper, video. CMU RI, vision.
• Ballester, Irene, et al., “DOT: Dynamic Object Tracking for Visual SLAM,” ICRA 2021
  • code, video, University of Zaragoza, vision

• Liu Yubao and Miura Jun, “RDMO-SLAM: Real-Time Visual SLAM for Dynamic Environments Using Semantic Label Prediction With Optical Flow,” IEEE Access.

• Kim Aleksandr, et al., “EagerMOT: 3D Multi-Object Tracking via Sensor Fusion,” ICRA 2021
  • TUM, code, video, sensor fusion.
• Shan, Mo, et al., “OrcVIO: Object Residual Constrained Visual-Inertial Odometry,” IROS2020

  • Shan, Mo, et al., “OrcVIO: Object Residual Constrained Visual-Inertial Odometry,” IROS 2021

  • code, video, Project page.

• Rosen, David M., et al., “Towards Lifelong Feature-Based Mapping in Semi-Static Environments,” ICRA 2016.

• Henein Mina, et al., “Dynamic SLAM: The Need For Speed,” ICRA 2020.
  • Zhang Jun, et al., “VDO-SLAM: A Visual Dynamic Object-Aware SLAM System,” ArXiv 2020.

  • “Robust Ego and Object 6-DoF Motion Estimation and Tracking,” Jun Zhang, Mina Henein, Robert Mahony, and Viorela Ila, IROS 2020 (code)

  • code, video, vision

• Minoda, Koji, et al., “VIODE: A Simulated Dataset to Address the Challenges of Visual-Inertial Odometry in Dynamic Environments,” RAL 2021

  • code and dataset, video.

• Vincent, Jonathan, et al., “Dynamic Object Tracking and Masking for Visual SLAM,” IROS 2020

  • code, video,

• Huang, Jiahui, et al., “ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings,” CVPR 2020

  • Tsinghua, code, video, slides.

• Liu, Yuzhen, et al., “A Switching-Coupled Backend for Simultaneous Localization and Dynamic Object Tracking,” RAL 2021

  • Tsinghua

• Yang Charig, et al., “Self-Supervised Video Object Segmentation by Motion Grouping,” ICCV 2021

  • Project page.

• Long Ran, et al., “RigidFusion: Robot Localisation and Mapping in Environments with Large Dynamic Rigid Objects,” RAL 2021

  • project page, code, video,

• Yang Bohong, et al., “Multi-Classes and Motion Properties for Concurrent Visual SLAM in Dynamic Environments,” IEEE Transactions on Multimedia, 2021

• Yang Gengshan and Ramanan Deva, “Learning to Segment Rigid Motions from Two Frames,” CVPR 2021

  • Project page.

• Thomas Hugues, et al., “Learning Spatiotemporal Occupancy Grid Maps for Lifelong Navigation in Dynamic Scenes,”

  • code.

• Jung Dongki, et al., “DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes,” ICCV 2021

  • code, video.

• Luiten Jonathon, et al., “Track to Reconstruct and Reconstruct to Track,” RAL+ICRA 2020

  • code, video, Reconstruct.

• Grinvald, Margarita, et al., “TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction,” ICRA 2021

  • code, video.

• Wang Chieh-Chih, et al., “Simultaneous Localization, Mapping and Moving Object Tracking,” The International Journal of Robotics Research, 2007

• Ran Teng, et al., “RS-SLAM: A Robust Semantic SLAM in Dynamic Environments Based on RGB-D Sensor.”

• Xu Hua, et al., “OD-SLAM: Real-Time Localization and Mapping in Dynamic Environment through Multi-Sensor Fusion,” (ICARM 2020) https://doi.org/10.1109/ICARM49381.2020.9195374.

• Wimbauer Felix, et al., “MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera,” CVPR 2021

  • Project page, code, video, video 2.

• Liu Yu, et al., “Dynamic RGB-D SLAM Based on Static Probability and Observation Number,” IEEE Transactions on Instrumentation and Measurement, vol. 70, 2021, pp. 1–11. IEEE Xplore, https://doi.org/10.1109/TIM.2021.3089228.

• P. Li, T. Qin, and S. Shen, “Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving,” arXiv 2018

  • Shen Shaojie’s group

• G. B. Nair et al., “Multi-object Monocular SLAM for Dynamic Environments,” IV2020

• M. Rünz and L. Agapito, “Co-fusion: Real-time segmentation, tracking and fusion of multiple objects,” in 2017 IEEE International Conference on Robotics and Automation (ICRA), May 2017, pp. 4471–4478.

  • code,
• (IROS 2022) TwistSLAM: Constrained SLAM in Dynamic Environment,
  • Follow-up to S3LAM, uses panoramic segmentation as the detection front-end

Other Application

• 3D VSG: Long-term Semantic Scene Change Prediction through 3D Variable Scene Graphs (2022)

  • Semantic scene change detection
  • code: https://github.com/ethz-asl/3d_vsg
• CubeSLAM: Monocular 3D Object SLAM, IEEE Transactions on Robotics 2019, S. Yang, S. Scherer PDF
  • code, video, vision
• Salas-Moreno Renato F., et al., “SLAM++: Simultaneous Localisation and Mapping at the Level of Objects,” CVPR 2013
  • code, video,
• Nicholson Lachlan, et al., “QuadricSLAM: Dual Quadrics From Object Detections as Landmarks in Object-Oriented SLAM,” RAL-2018
  • Project page, code.
• Wu Yanmin, et al., “EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association,” 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2020, pp. 4966–73. arXiv.org, https://doi.org/10.1109/IROS45743.2020.9341757.
  • Project page, code.
• H. Osman, N. Darwish, and A. Bayoumi, “LoopNet: Where to Focus Detecting Loop Closures in Dynamic Scenes,” IEEE Robotics and Automation Letters, pp. 1–1, 2022, doi: 10.1109/LRA.2022.3142901.
  • Loop detection network in dynamic environments, code, video
• M. N. Finean, L. Petrović, W. Merkt, I. Marković, and I. Havoutis, “Motion Planning in Dynamic Environments Using Context-Aware Human Trajectory Prediction,” arXiv:2201.05058 [cs], Jan. 2022.
  • Navigation in dynamic environments with humans, code, video. Similar to obstacle avoidance and planning.

• (IROS 2022) Extrinsic Camera Calibration from A Moving Person

• (IROS 2022) ACEFusion: Accelerated and Energy-Efficient Semantic 3D Reconstruction of Dynamic Scenes

• (IROS 2022) Efficient 2D LIDAR-Based Map Updating For Long-Term Operations in Dynamic Environments

• (IROS 2022) Detecting Invalid Map Merges in Lifelong SLAM

• (IROS 2022) Probabilistic Object Maps for Long-Term Robot Localization

• (IROS 2022) ROLL: Long-Term Robust LiDAR-based Localization With Temporary Mapping in Changing Environments

GitHub Stars

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