Constrained Visual-Inertial Localization With Application And Benchmark in Laparoscopic Surgery

TL;DR

This paper introduces a constrained visual-inertial localization method tailored for minimally invasive surgery, utilizing residuals from multiple modalities to improve accuracy and robustness in dynamic, constrained environments.

Contribution

The paper presents a novel joint optimization approach combining IMU, stereoscopic features, and constraints, along with a new clinical dataset for laparoscopic surgery localization.

Findings

Method improves localization accuracy in dynamic surgical settings

Residual-based optimization reduces problem complexity

New dataset MITI enables benchmarking in minimally invasive surgery

Abstract

We propose a novel method to tackle the visual-inertial localization problem for constrained camera movements. We use residuals from the different modalities to jointly optimize a global cost function. The residuals emerge from IMU measurements, stereoscopic feature points, and constraints on possible solutions in SE(3). In settings where dynamic disturbances are frequent, the residuals reduce the complexity of the problem and make localization feasible. We verify the advantages of our method in a suitable medical use case and produce a dataset capturing a minimally invasive surgery in the abdomen. Our novel clinical dataset MITI is comparable to state-of-the-art evaluation datasets, contains calibration and synchronization and is available at https://mediatum.ub.tum.de/1621941.