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.

翻译：我们提出了一种新的基准测试，用于评估针对腹部微创手术（MIS）的立体视觉-惯性计算机视觉算法（SLAM/SfM/三维重建/视觉-惯性里程计）。我们的MITI数据集发布于[https://mediatum.ub.tum.de/1621941]，通过完整记录在慕尼黑工业大学伊萨尔右岸研究医院进行的一次手持式手术介入，提供了所有必要数据。它包含来自IMU、立体视频以及作为评估基准的红外（IR）跟踪的多模态传感器信息。此外，我们还提供了立体镜、加速度计、磁力计的校准参数、传感器设置中的刚性变换以及时间偏移量。我们精心选择了一次包含极少切割和组织变形、并使用手持式摄像头完成腹部全景扫描的手术介入，这使其成为测试SLAM算法的理想场景。本数据集旨在推动专为MIS应用设计的视觉-惯性算法的发展，我们希望这一临床训练数据集能够帮助并赋能研究者改进相关算法。