# A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers

arXiv: https://arxiv.org/abs/2207.00254 · HTML: https://mlsanity.com/paper/2207.00254/

## Key facts

- Cited by 66 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2022.

## Built on by (most cited first, 30 of 66)

- [A Survey on 3D Gaussian Splatting](https://mlsanity.com/paper/2401.03890/) (2024)
- [How NeRFs and 3D Gaussian Splatting are Reshaping SLAM: a Survey](https://mlsanity.com/paper/2402.13255/) (2024)
- [Active Neural Mapping](https://mlsanity.com/paper/2308.16246/) (2023)
- [ActiveRMAP: Radiance Field for Active Mapping And Planning](https://mlsanity.com/paper/2211.12656/) (2022)
- [NARUTO: Neural Active Reconstruction from Uncertain Target Observations](https://mlsanity.com/paper/2402.18771/) (2024)
- [ActiveGAMER: Active GAussian Mapping through Efficient Rendering](https://mlsanity.com/paper/2501.06897/) (2025)
- [POp-GS: Next Best View in 3D-Gaussian Splatting with P-Optimality](https://mlsanity.com/paper/2503.07819/) (2025)
- [iSLAM: Imperative SLAM](https://mlsanity.com/paper/2306.07894/) (2023)
- [CMRNext: Camera to LiDAR Matching in the Wild for Localization and Extrinsic Calibration](https://mlsanity.com/paper/2402.00129/) (2024)
- [NextBestPath: Efficient 3D Mapping of Unseen Environments](https://mlsanity.com/paper/2502.05378/) (2025)
- [AcTExplore: Active Tactile Exploration of Unknown Objects](https://mlsanity.com/paper/2310.08745/) (2023)
- [Modeling Uncertainty in 3D Gaussian Splatting through Continuous Semantic Splatting](https://mlsanity.com/paper/2411.02547/) (2024)
- [Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities](https://mlsanity.com/paper/2310.08565/) (2023)
- [DGNR: Density-Guided Neural Point Rendering of Large Driving Scenes](https://mlsanity.com/paper/2311.16664/) (2023)
- [Multimodal LLM Guided Exploration and Active Mapping using Fisher Information](https://mlsanity.com/paper/2410.17422/) (2024)
- [Metrically Scaled Monocular Depth Estimation through Sparse Priors for Underwater Robots](https://mlsanity.com/paper/2310.16750/) (2023)
- [LOC-ZSON: Language-driven Object-Centric Zero-Shot Object Retrieval and Navigation](https://mlsanity.com/paper/2405.05363/) (2024)
- [Understanding while Exploring: Semantics-driven Active Mapping](https://mlsanity.com/paper/2506.00225/) (2025)
- [Uncertainty-driven 3D Gaussian Splatting Active Mapping via Anisotropic Visibility Field](https://mlsanity.com/paper/2605.30342/) (2026)
- [Learning Continuous Control Policies for Information-Theoretic Active Perception](https://mlsanity.com/paper/2209.12427/) (2022)
- [Simplified Continuous High Dimensional Belief Space Planning with Adaptive Probabilistic Belief-dependent Constraints](https://mlsanity.com/paper/2302.06697/) (2023)
- [MAD-ICP: It Is All About Matching Data -- Robust and Informed LiDAR Odometry](https://mlsanity.com/paper/2405.05828/) (2024)
- [Learning Where to Look: Self-supervised Viewpoint Selection for Active Localization using Geometrical Information](https://mlsanity.com/paper/2407.15593/) (2024)
- [Monocular Visual Place Recognition in LiDAR Maps via Cross-Modal State Space Model and Multi-View Matching](https://mlsanity.com/paper/2410.06285/) (2024)
- [Zero-shot Object-Centric Instruction Following: Integrating Foundation Models with Traditional Navigation](https://mlsanity.com/paper/2411.07848/) (2024)
- [3D Scene Graphs: Open Challenges and Future Directions](https://mlsanity.com/paper/2606.19383/) (2026)
- [Policy Learning for Active Target Tracking over Continuous SE(3) Trajectories](https://mlsanity.com/paper/2212.01498/) (2022)
- [Gradient-based Local Next-best-view Planning for Improved Perception of Targeted Plant Nodes](https://mlsanity.com/paper/2311.16759/) (2023)
- [A Survey on Reinforcement Learning Applications in SLAM](https://mlsanity.com/paper/2408.14518/) (2024)
- [Learning Dynamic Cognitive Map with Autonomous Navigation](https://mlsanity.com/paper/2411.08447/) (2024)

## Abstract (arXiv)

Active Simultaneous Localization and Mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities. This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers. In this work, we survey the state-of-the-art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications. After providing a historical perspective, we present a unified problem formulation and review the well-established modular solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including belief-space planning and deep reinforcement learning techniques, and review related work on multi-robot coordination. The manuscript concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics.