# Visual-Inertial SLAM for Unstructured Outdoor Environments: Benchmarking the Benefits and Computational Costs of Loop Closing

arXiv: https://arxiv.org/abs/2408.01716 · HTML: https://mlsanity.com/paper/2408.01716/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 73 references that are in the map.
- First posted on arXiv 3 Aug 2024.

## Builds on (most cited first, 28 of 28)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs](https://mlsanity.com/paper/2101.06894/) (2021)
- [A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors](https://mlsanity.com/paper/1901.03638/) (2019)
- [SkyEye: Self-Supervised Bird's-Eye-View Semantic Mapping Using Monocular Frontal View Images](https://mlsanity.com/paper/2302.04233/) (2023)
- [The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection](https://mlsanity.com/paper/2204.12831/) (2022)
- [Dynamic Object Removal and Spatio-Temporal RGB-D Inpainting via Geometry-Aware Adversarial Learning](https://mlsanity.com/paper/2008.05058/) (2020)
- [Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping Through Continual Learning](https://mlsanity.com/paper/2203.01578/) (2022)
- [GVINS: Tightly Coupled GNSS-Visual-Inertial Fusion for Smooth and Consistent State Estimation](https://mlsanity.com/paper/2103.07899/) (2021)
- [Experimental Comparison of Open Source Visual-Inertial-Based State Estimation Algorithms in the Underwater Domain](https://mlsanity.com/paper/1904.02215/) (2019)
- [CMRNext: Camera to LiDAR Matching in the Wild for Localization and Extrinsic Calibration](https://mlsanity.com/paper/2402.00129/) (2024)
- [maplab 2.0 -- A Modular and Multi-Modal Mapping Framework](https://mlsanity.com/paper/2212.00654/) (2022)
- [The Rosario Dataset: Multisensor Data for Localization and Mapping in Agricultural Environments](https://mlsanity.com/paper/1809.06413/) (2018)
- [A Comparison of Modern General-Purpose Visual SLAM Approaches](https://mlsanity.com/paper/2107.07589/) (2021)
- [CoVIO: Online Continual Learning for Visual-Inertial Odometry](https://mlsanity.com/paper/2303.10149/) (2023)
- [Robust SLAM Systems: Are We There Yet?](https://mlsanity.com/paper/2109.13160/) (2021)
- [Experimental Comparison of Visual-Aided Odometry Methods for Rail Vehicles](https://mlsanity.com/paper/1904.00936/) (2019)
- [Benchmark of visual and 3D lidar SLAM systems in simulation environment for vineyards](https://mlsanity.com/paper/2107.05283/) (2021)
- [OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics](https://mlsanity.com/paper/2203.00403/) (2022)
- [Experimental Evaluation of Visual-Inertial Odometry Systems for Arable Farming](https://mlsanity.com/paper/2206.05066/) (2022)
- [RaLF: Flow-based Global and Metric Radar Localization in LiDAR Maps](https://mlsanity.com/paper/2309.09875/) (2023)
- [Comparison of modern open-source visual SLAM approaches](https://mlsanity.com/paper/2108.01654/) (2021)
- [Closed-Loop Benchmarking of Stereo Visual-Inertial SLAM Systems: Understanding the Impact of Drift and Latency on Tracking Accuracy](https://mlsanity.com/paper/2003.01317/) (2020)

## Built on by (most cited first, 4 of 4)

- [Visual Loop Closure Detection Through Deep Graph Consensus](https://mlsanity.com/paper/2505.21754/) (2025)
- [ICD-Net: Inertial Covariance Displacement Network for Drone Visual-Inertial SLAM](https://mlsanity.com/paper/2512.00037/) (2025)
- [Sensor Configuration Matters: A Systematic Evaluation of Multimodal SLAM on Quadruped Robots](https://mlsanity.com/paper/2606.19067/) (2026)
- [Pocket-SLAM: Rendering-Area-Aware Pruning for Memory-Efficient 3DGS-SLAM](https://mlsanity.com/paper/2606.24796/) (2026)

## Abstract (arXiv)

Simultaneous Localization and Mapping (SLAM) is essential for mobile robotics, enabling autonomous navigation in dynamic, unstructured outdoor environments without relying on external positioning systems. These environments pose significant challenges due to variable lighting, weather conditions, and complex terrain. Visual-Inertial SLAM has emerged as a promising solution for robust localization under such conditions. This paper benchmarks several open-source Visual-Inertial SLAM systems, including traditional methods (ORB-SLAM3, VINS-Fusion, OpenVINS, Kimera, and SVO Pro) and learning-based approaches (HFNet-SLAM, AirSLAM), to evaluate their performance in unstructured natural outdoor settings. We focus on the impact of loop closing on localization accuracy and computational demands, providing a comprehensive analysis of these systems' effectiveness in real-world environments and especially their application to embedded systems in outdoor robotics. Our contributions further include an assessment of varying frame rates on localization accuracy and computational load. The findings highlight the importance of loop closing in improving localization accuracy while managing computational resources efficiently, offering valuable insights for optimizing Visual-Inertial SLAM systems for practical outdoor applications in mobile robotics. The dataset and the benchmark code are available under https://github.com/iis-esslingen/vi-slam_lc_benchmark.