Constrained Visual-Inertial Localization With Application And Benchmark in Laparoscopic Surgery
This addresses localization challenges in laparoscopic surgery, where camera movements are constrained and dynamic disturbances are frequent, though it appears incremental as it builds on existing visual-inertial methods with added constraints.
The paper tackles visual-inertial localization for constrained camera movements by jointly optimizing residuals from IMU measurements, stereoscopic features, and SE(3) constraints, making localization feasible in dynamic settings; it introduces the MITI dataset for laparoscopic surgery, comparable to state-of-the-art benchmarks.
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.