# MITI: SLAM Benchmark for Laparoscopic Surgery

arXiv: https://arxiv.org/abs/2202.11496 · HTML: https://mlsanity.com/paper/2202.11496/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 6 references that are in the map.
- First posted on arXiv 23 Feb 2022.

## Builds on (most cited first, 2 of 2)

- [A micro Lie theory for state estimation in robotics](https://mlsanity.com/paper/1812.01537/) (2018)
- [Efficient Derivative Computation for Cumulative B-Splines on Lie Groups](https://mlsanity.com/paper/1911.08860/) (2019)

## Built on by (most cited first, 2 of 2)

- [RIDE: Self-Supervised Learning of Rotation-Equivariant Keypoint Detection and Invariant Description for Endoscopy](https://mlsanity.com/paper/2309.09563/) (2023)
- [Towards Next-Generation Healthcare: A Survey of Medical Embodied AI for Perception, Decision-Making, and Action](https://mlsanity.com/paper/2606.15647/) (2026)

## Abstract (arXiv)

We propose a new benchmark for evaluating stereoscopic visual-inertial computer vision algorithms (SLAM/ SfM/ 3D Reconstruction/ Visual-Inertial Odometry) for minimally invasive surgical (MIS) interventions in the abdomen. Our MITI Dataset available at [https://mediatum.ub.tum.de/1621941] provides all the necessary data by a complete recording of a handheld surgical intervention at Research Hospital Rechts der Isar of TUM. It contains multimodal sensor information from IMU, stereoscopic video, and infrared (IR) tracking as ground truth for evaluation. Furthermore, calibration for the stereoscope, accelerometer, magnetometer, the rigid transformations in the sensor setup, and time-offsets are available. We wisely chose a suitable intervention that contains very few cutting and tissue deformation and shows a full scan of the abdomen with a handheld camera such that it is ideal for testing SLAM algorithms. Intending to promote the progress of visual-inertial algorithms designed for MIS application, we hope that our clinical training dataset helps and enables researchers to enhance algorithms.