MITI: SLAM Benchmark for Laparoscopic Surgery

TL;DR

This paper introduces MITI, a comprehensive benchmark dataset for evaluating stereoscopic visual-inertial SLAM algorithms in minimally invasive abdominal surgery, facilitating advancements in surgical navigation technology.

Contribution

It provides a new multimodal dataset with synchronized sensor data, ground truth, and calibration details specifically designed for MIS SLAM algorithm evaluation.

Findings

Dataset includes multimodal sensor data from real surgical procedures.

Provides ground truth and calibration for accurate algorithm assessment.

Aims to accelerate development of SLAM algorithms for surgical applications.

Abstract

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.