# Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping Through Continual Learning

arXiv: https://arxiv.org/abs/2203.01578 · HTML: https://mlsanity.com/paper/2203.01578/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2022.

## Built on by (most cited first, 19 of 19)

- [SkyEye: Self-Supervised Bird's-Eye-View Semantic Mapping Using Monocular Frontal View Images](https://mlsanity.com/paper/2302.04233/) (2023)
- [Learning and Aggregating Lane Graphs for Urban Automated Driving](https://mlsanity.com/paper/2302.06175/) (2023)
- [CoDEPS: Online Continual Learning for Depth Estimation and Panoptic Segmentation](https://mlsanity.com/paper/2303.10147/) (2023)
- [CMRNext: Camera to LiDAR Matching in the Wild for Localization and Extrinsic Calibration](https://mlsanity.com/paper/2402.00129/) (2024)
- [AmodalSynthDrive: A Synthetic Amodal Perception Dataset for Autonomous Driving](https://mlsanity.com/paper/2309.06547/) (2023)
- [Panoptic Out-of-Distribution Segmentation](https://mlsanity.com/paper/2310.11797/) (2023)
- [A Point-Based Approach to Efficient LiDAR Multi-Task Perception](https://mlsanity.com/paper/2404.12798/) (2024)
- [GeoAdapt: Self-Supervised Test-Time Adaptation in LiDAR Place Recognition Using Geometric Priors](https://mlsanity.com/paper/2308.04638/) (2023)
- [Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications](https://mlsanity.com/paper/2410.09218/) (2024)
- [A Good Foundation is Worth Many Labels: Label-Efficient Panoptic Segmentation](https://mlsanity.com/paper/2405.19035/) (2024)
- [RaLF: Flow-based Global and Metric Radar Localization in LiDAR Maps](https://mlsanity.com/paper/2309.09875/) (2023)
- [Continual Learning of Unsupervised Monocular Depth from Videos](https://mlsanity.com/paper/2311.02393/) (2023)
- [Visual-Inertial SLAM for Unstructured Outdoor Environments: Benchmarking the Benefits and Computational Costs of Loop Closing](https://mlsanity.com/paper/2408.01716/) (2024)
- [ROVER: A Multi-Season Dataset for Visual SLAM](https://mlsanity.com/paper/2412.02506/) (2024)
- [LetsMap: Unsupervised Representation Learning for Semantic BEV Mapping](https://mlsanity.com/paper/2405.18852/) (2024)
- [Task-agnostic Lifelong Robot Learning with Retrieval-based Weighted Local Adaptation](https://mlsanity.com/paper/2410.02995/) (2024)
- [SLAM in the Dark: Self-Supervised Learning of Pose, Depth and Loop-Closure from Thermal Images](https://mlsanity.com/paper/2502.18932/) (2025)
- [An Online Adaptation Method for Robust Depth Estimation and Visual Odometry in the Open World](https://mlsanity.com/paper/2504.11698/) (2025)
- [SLAM: Structured and Localized Analytic Manifold Adaptation for Forgetting-Immune and Domain-Robust Lifelong VPR](https://mlsanity.com/paper/2607.04764/) (2026)

## Abstract (arXiv)

Robots operating in the open world encounter various different environments that can substantially differ from each other. This domain gap also poses a challenge for Simultaneous Localization and Mapping (SLAM) being one of the fundamental tasks for navigation. In particular, learning-based SLAM methods are known to generalize poorly to unseen environments hindering their general adoption. In this work, we introduce the novel task of continual SLAM extending the concept of lifelong SLAM from a single dynamically changing environment to sequential deployments in several drastically differing environments. To address this task, we propose CL-SLAM leveraging a dual-network architecture to both adapt to new environments and retain knowledge with respect to previously visited environments. We compare CL-SLAM to learning-based as well as classical SLAM methods and show the advantages of leveraging online data. We extensively evaluate CL-SLAM on three different datasets and demonstrate that it outperforms several baselines inspired by existing continual learning-based visual odometry methods. We make the code of our work publicly available at http://continual-slam.cs.uni-freiburg.de.