# Advances in Inference and Representation for Simultaneous Localization and Mapping

arXiv: https://arxiv.org/abs/2103.05041 · HTML: https://mlsanity.com/paper/2103.05041/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 55 of its 148 references that are in the map.
- First posted on arXiv 8 Mar 2021.

## Builds on (most cited first, 30 of 55)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera](https://mlsanity.com/paper/1910.02527/) (2019)
- [TEASER: Fast and Certifiable Point Cloud Registration](https://mlsanity.com/paper/2001.07715/) (2020)
- [Neural Topological SLAM for Visual Navigation](https://mlsanity.com/paper/2005.12256/) (2020)
- [Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping](https://mlsanity.com/paper/1910.02490/) (2019)
- [3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans](https://mlsanity.com/paper/2002.06289/) (2020)
- [SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networks](https://mlsanity.com/paper/1609.05130/) (2016)
- [DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes](https://mlsanity.com/paper/1806.05620/) (2018)
- [DeepFactors: Real-Time Probabilistic Dense Monocular SLAM](https://mlsanity.com/paper/2001.05049/) (2020)
- [Learning to Navigate in Cities Without a Map](https://mlsanity.com/paper/1804.00168/) (2018)
- [The Limits and Potentials of Deep Learning for Robotics](https://mlsanity.com/paper/1804.06557/) (2018)
- [Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection](https://mlsanity.com/paper/1909.08605/) (2019)
- [SE-Sync: A Certifiably Correct Algorithm for Synchronization over the Special Euclidean Group](https://mlsanity.com/paper/1612.07386/) (2016)
- [Interactive Perception: Leveraging Action in Perception and Perception in Action](https://mlsanity.com/paper/1604.03670/) (2016)
- [NodeSLAM: Neural Object Descriptors for Multi-View Shape Reconstruction](https://mlsanity.com/paper/2004.04485/) (2020)
- [LDSO: Direct Sparse Odometry with Loop Closure](https://mlsanity.com/paper/1808.01111/) (2018)
- [Learning Physical Graph Representations from Visual Scenes](https://mlsanity.com/paper/2006.12373/) (2020)
- [Shonan Rotation Averaging: Global Optimality by Surfing SO(p)n](https://mlsanity.com/paper/2008.02737/) (2020)
- [VDO-SLAM: A Visual Dynamic Object-aware SLAM System](https://mlsanity.com/paper/2005.11052/) (2020)
- [Toward Geometric Deep SLAM](https://mlsanity.com/paper/1707.07410/) (2017)
- [SceneCode: Monocular Dense Semantic Reconstruction using Learned Encoded Scene Representations](https://mlsanity.com/paper/1903.06482/) (2019)
- [FutureMapping: The Computational Structure of Spatial AI Systems](https://mlsanity.com/paper/1803.11288/) (2018)
- [Visual-Inertial Navigation: A Concise Review](https://mlsanity.com/paper/1906.02650/) (2019)
- [gradSLAM: Automagically differentiable SLAM](https://mlsanity.com/paper/1910.10672/) (2019)

## Built on by (most cited first, 16 of 16)

- [Density-aware NeRF Ensembles: Quantifying Predictive Uncertainty in Neural Radiance Fields](https://mlsanity.com/paper/2209.08718/) (2022)
- [Advances in Global Solvers for 3D Vision](https://mlsanity.com/paper/2602.14662/) (2026)
- [Open-Vocabulary Online Semantic Mapping for SLAM](https://mlsanity.com/paper/2411.15043/) (2024)
- [Towards Initialization-free Calibrated Bundle Adjustment](https://mlsanity.com/paper/2506.23808/) (2025)
- [3D Scene Graphs: Open Challenges and Future Directions](https://mlsanity.com/paper/2606.19383/) (2026)
- [Discrete-Continuous Smoothing and Mapping](https://mlsanity.com/paper/2204.11936/) (2022)
- [Accelerating Certifiable Estimation with Preconditioned Eigensolvers](https://mlsanity.com/paper/2207.05257/) (2022)
- [Decentralization and Acceleration Enables Large-Scale Bundle Adjustment](https://mlsanity.com/paper/2305.07026/) (2023)
- [Certifiably Optimal Anisotropic Rotation Averaging](https://mlsanity.com/paper/2503.07353/) (2025)
- [Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows](https://mlsanity.com/paper/2110.00876/) (2021)
- [Streaming Inference for Infinite Non-Stationary Clustering](https://mlsanity.com/paper/2205.01212/) (2022)
- [A Review of Scene Representations for Robot Manipulators](https://mlsanity.com/paper/2301.11275/) (2022)
- [Ray-Patch: An Efficient Querying for Light Field Transformers](https://mlsanity.com/paper/2305.09566/) (2023)
- [Multi-Irreducible Spectral Synchronization for Robust Rotation Averaging](https://mlsanity.com/paper/2311.16544/) (2023)
- [Deep Learning Perspective of Scene Understanding in Autonomous Robots](https://mlsanity.com/paper/2512.14020/) (2025)
- [Certifiable Factor Graph Optimization](https://mlsanity.com/paper/2603.01267/) (2026)

## Abstract (arXiv)

Simultaneous localization and mapping (SLAM) is the process of constructing a global model of an environment from local observations of it; this is a foundational capability for mobile robots, supporting such core functions as planning, navigation, and control. This article reviews recent progress in SLAM, focusing on advances in the expressive capacity of the environmental models used in SLAM systems (representation) and the performance of the algorithms used to estimate these models from data (inference). A prominent theme of recent SLAM research is the pursuit of environmental representations (including learned representations) that go beyond the classical attributes of geometry and appearance to model properties such as hierarchical organization, affordance, dynamics, and semantics; these advances equip autonomous agents with a more comprehensive understanding of the world, enabling more versatile and intelligent operation. A second major theme is a revitalized interest in the mathematical properties of the SLAM estimation problem itself (including its computational and information-theoretic performance limits); this work has led to the development of novel classes of certifiable and robust inference methods that dramatically improve the reliability of SLAM systems in real-world operation. We survey these advances with an emphasis on their ramifications for achieving robust, long-duration autonomy, and conclude with a discussion of open challenges and a perspective on future research directions.