# Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM

arXiv: https://arxiv.org/abs/1911.05603 · HTML: https://mlsanity.com/paper/1911.05603/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2019.

## Built on by (most cited first, 30 of 42)

- [RDumb: A simple approach that questions our progress in continual test-time adaptation](https://mlsanity.com/paper/2306.05401/) (2023)
- [Continuous Coordination As a Realistic Scenario for Lifelong Learning](https://mlsanity.com/paper/2103.03216/) (2021)
- [Towards Robust Robot 3D Perception in Urban Environments: The UT Campus Object Dataset](https://mlsanity.com/paper/2309.13549/) (2023)
- [M2DGR: A Multi-sensor and Multi-scenario SLAM Dataset for Ground Robots](https://mlsanity.com/paper/2112.13659/) (2021)
- [Embodied AI: From LLMs to World Models](https://mlsanity.com/paper/2509.20021/) (2025)
- [DDN-SLAM: Real-time Dense Dynamic Neural Implicit SLAM](https://mlsanity.com/paper/2401.01545/) (2024)
- [General Place Recognition Survey: Towards the Real-world Autonomy Age](https://mlsanity.com/paper/2209.04497/) (2022)
- [Visual SLAM: What are the Current Trends and What to Expect?](https://mlsanity.com/paper/2210.10491/) (2022)
- [Semantic Visual Simultaneous Localization and Mapping: A Survey](https://mlsanity.com/paper/2209.06428/) (2022)
- [Interactive Continual Learning Architecture for Long-Term Personalization of Home Service Robots](https://mlsanity.com/paper/2403.03462/) (2024)
- [LatentSLAM: unsupervised multi-sensor representation learning for localization and mapping](https://mlsanity.com/paper/2105.03265/) (2021)
- [VSLAM-LAB: A Comprehensive Framework for Visual SLAM Methods and Datasets](https://mlsanity.com/paper/2504.04457/) (2025)
- [Multi-Session Visual SLAM for Illumination Invariant Re-Localization in Indoor Environments](https://mlsanity.com/paper/2103.03827/) (2021)
- [Comparison of modern open-source visual SLAM approaches](https://mlsanity.com/paper/2108.01654/) (2021)
- [SelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning](https://mlsanity.com/paper/2203.05332/) (2022)
- [RWT-SLAM: Robust Visual SLAM for Highly Weak-textured Environments](https://mlsanity.com/paper/2207.03539/) (2022)
- [NeuV-SLAM: Fast Neural Multiresolution Voxel Optimization for RGBD Dense SLAM](https://mlsanity.com/paper/2402.02020/) (2024)
- [Monocular visual simultaneous localization and mapping: (r)evolution from geometry to deep learning-based pipelines](https://mlsanity.com/paper/2503.02955/) (2025)
- [Partially Observable Adversarial Patch Attacks on Vision-Language-Action Models in Robotics](https://mlsanity.com/paper/2606.03556/) (2026)
- [RP-VIO: Robust Plane-based Visual-Inertial Odometry for Dynamic Environments](https://mlsanity.com/paper/2103.10400/) (2021)
- [Self-Supervised Depth Completion for Active Stereo](https://mlsanity.com/paper/2110.03234/) (2021)
- [Camera Motion Estimation from RGB-D-Inertial Scene Flow](https://mlsanity.com/paper/2404.17251/) (2024)
- [Benchmarking Pedestrian Odometry: The Brown Pedestrian Odometry Dataset (BPOD)](https://mlsanity.com/paper/2112.13018/) (2021)
- [A Benchmark Dataset for Collaborative SLAM in Service Environments](https://mlsanity.com/paper/2411.14775/) (2024)
- [From Gaming to Research: GTA V for Synthetic Data Generation for Robotics and Navigations](https://mlsanity.com/paper/2502.12303/) (2025)
- [vS-Graphs: Tightly Coupling Visual SLAM and 3D Scene Graphs Exploiting Hierarchical Scene Understanding](https://mlsanity.com/paper/2503.01783/) (2025)
- [SLAM&Render: A Benchmark for the Intersection Between Neural Rendering, Gaussian Splatting and SLAM](https://mlsanity.com/paper/2504.13713/) (2025)
- [A Collaborative Visual SLAM Framework for Service Robots](https://mlsanity.com/paper/2102.03228/) (2021)
- [BS3D: Building-scale 3D Reconstruction from RGB-D Images](https://mlsanity.com/paper/2301.01057/) (2023)
- [QueensCAMP: an RGB-D dataset for robust Visual SLAM](https://mlsanity.com/paper/2410.12520/) (2024)

## Abstract (arXiv)

Service robots should be able to operate autonomously in dynamic and daily changing environments over an extended period of time. While Simultaneous Localization And Mapping (SLAM) is one of the most fundamental problems for robotic autonomy, most existing SLAM works are evaluated with data sequences that are recorded in a short period of time. In real-world deployment, there can be out-of-sight scene changes caused by both natural factors and human activities. For example, in home scenarios, most objects may be movable, replaceable or deformable, and the visual features of the same place may be significantly different in some successive days. Such out-of-sight dynamics pose great challenges to the robustness of pose estimation, and hence a robot's long-term deployment and operation. To differentiate the forementioned problem from the conventional works which are usually evaluated in a static setting in a single run, the term textitlifelong SLAM is used here to address SLAM problems in an ever-changing environment over a long period of time. To accelerate lifelong SLAM research, we release the OpenLORIS-Scene datasets. The data are collected in real-world indoor scenes, for multiple times in each place to include scene changes in real life. We also design benchmarking metrics for lifelong SLAM, with which the robustness and accuracy of pose estimation are evaluated separately. The datasets and benchmark are available online at https://lifelong-robotic-vision.github.io/dataset/scene.