# The TUM VI Benchmark for Evaluating Visual-Inertial Odometry

arXiv: https://arxiv.org/abs/1804.06120 · HTML: https://mlsanity.com/paper/1804.06120/

## Key facts

- Cited by 99 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2018.

## Built on by (most cited first, 30 of 99)

- [TUM-VIE: The TUM Stereo Visual-Inertial Event Dataset](https://mlsanity.com/paper/2108.07329/) (2021)
- [The Oxford Spires Dataset: Benchmarking Large-Scale LiDAR-Visual Localisation, Reconstruction and Radiance Field Methods](https://mlsanity.com/paper/2411.10546/) (2024)
- [IDOL: Inertial Deep Orientation-Estimation and Localization](https://mlsanity.com/paper/2102.04024/) (2021)
- [M2DGR: A Multi-sensor and Multi-scenario SLAM Dataset for Ground Robots](https://mlsanity.com/paper/2112.13659/) (2021)
- [DM-VIO: Delayed Marginalization Visual-Inertial Odometry](https://mlsanity.com/paper/2201.04114/) (2022)
- [MM3DGS SLAM: Multi-modal 3D Gaussian Splatting for SLAM Using Vision, Depth, and Inertial Measurements](https://mlsanity.com/paper/2404.00923/) (2024)
- [LIFT-SLAM: a deep-learning feature-based monocular visual SLAM method](https://mlsanity.com/paper/2104.00099/) (2021)
- [The Hilti SLAM Challenge Dataset](https://mlsanity.com/paper/2109.11316/) (2021)
- [Multi-Camera LiDAR Inertial Extension to the Newer College Dataset](https://mlsanity.com/paper/2112.08854/) (2021)
- [From SLAM to Situational Awareness: Challenges and Survey](https://mlsanity.com/paper/2110.00273/) (2021)
- [Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey](https://mlsanity.com/paper/2406.19675/) (2024)
- [BabelCalib: A Universal Approach to Calibrating Central Cameras](https://mlsanity.com/paper/2109.09704/) (2021)
- [Benchmarking Egocentric Visual-Inertial SLAM at City Scale](https://mlsanity.com/paper/2509.26639/) (2025)
- [LF-VISLAM: A SLAM Framework for Large Field-of-View Cameras with Negative Imaging Plane on Mobile Agents](https://mlsanity.com/paper/2209.05167/) (2022)
- [High Definition, Inexpensive, Underwater Mapping](https://mlsanity.com/paper/2203.05640/) (2022)
- [D3FlowSLAM: Self-Supervised Dynamic SLAM with Flow Motion Decomposition and DINO Guidance](https://mlsanity.com/paper/2207.08794/) (2022)
- [The Monado SLAM Dataset for Egocentric Visual-Inertial Tracking](https://mlsanity.com/paper/2508.00088/) (2025)
- [HybVIO: Pushing the Limits of Real-time Visual-inertial Odometry](https://mlsanity.com/paper/2106.11857/) (2021)
- [SmartPortraits: Depth Powered Handheld Smartphone Dataset of Human Portraits for State Estimation, Reconstruction and Synthesis](https://mlsanity.com/paper/2204.10211/) (2022)
- [Unifying Local and Global Multimodal Features for Place Recognition in Aliased and Low-Texture Environments](https://mlsanity.com/paper/2403.13395/) (2024)
- [RIANN -- A Robust Neural Network Outperforms Attitude Estimation Filters](https://mlsanity.com/paper/2104.07391/) (2021)
- [Square Root Marginalization for Sliding-Window Bundle Adjustment](https://mlsanity.com/paper/2109.02182/) (2021)
- [Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review](https://mlsanity.com/paper/2302.11089/) (2023)
- [AirIMU: Learning Uncertainty Propagation for Inertial Odometry](https://mlsanity.com/paper/2310.04874/) (2023)
- [4Seasons: Benchmarking Visual SLAM and Long-Term Localization for Autonomous Driving in Challenging Conditions](https://mlsanity.com/paper/2301.01147/) (2022)
- [The Drunkard's Odometry: Estimating Camera Motion in Deforming Scenes](https://mlsanity.com/paper/2306.16917/) (2023)
- [SchurVINS: Schur Complement-Based Lightweight Visual Inertial Navigation System](https://mlsanity.com/paper/2312.01616/) (2023)
- [DBA-Fusion: Tightly Integrating Deep Dense Visual Bundle Adjustment with Multiple Sensors for Large-Scale Localization and Mapping](https://mlsanity.com/paper/2403.13714/) (2024)
- [Comparison of modern open-source visual SLAM approaches](https://mlsanity.com/paper/2108.01654/) (2021)
- [Generalizable End-to-End Deep Learning Frameworks for Real-Time Attitude Estimation Using 6DoF Inertial Measurement Units](https://mlsanity.com/paper/2302.06037/) (2023)

## Abstract (arXiv)

Visual odometry and SLAM methods have a large variety of applications in domains such as augmented reality or robotics. Complementing vision sensors with inertial measurements tremendously improves tracking accuracy and robustness, and thus has spawned large interest in the development of visual-inertial (VI) odometry approaches. In this paper, we propose the TUM VI benchmark, a novel dataset with a diverse set of sequences in different scenes for evaluating VI odometry. It provides camera images with 1024x1024 resolution at 20 Hz, high dynamic range and photometric calibration. An IMU measures accelerations and angular velocities on 3 axes at 200 Hz, while the cameras and IMU sensors are time-synchronized in hardware. For trajectory evaluation, we also provide accurate pose ground truth from a motion capture system at high frequency (120 Hz) at the start and end of the sequences which we accurately aligned with the camera and IMU measurements. The full dataset with raw and calibrated data is publicly available. We also evaluate state-of-the-art VI odometry approaches on our dataset.