Magnetic-Visual Sensor Fusion based Medical SLAM for Endoscopic Capsule Robot
This addresses the need for reliable navigation in minimally invasive gastrointestinal diagnostics, though it appears incremental as it builds on existing sensor fusion and SLAM techniques.
The paper tackled the problem of real-time simultaneous localization and mapping (SLAM) for endoscopic capsule robots by proposing a dense, non-rigidly deformable map fusion method that combines magnetic and vision-based localization, achieving root mean square localization errors of 0.42 to 1.92 cm and surface reconstruction errors of 1.23 to 2.39 cm in ex-vivo tests.
A reliable, real-time simultaneous localization and mapping (SLAM) method is crucial for the navigation of actively controlled capsule endoscopy robots. These robots are an emerging, minimally invasive diagnostic and therapeutic technology for use in the gastrointestinal (GI) tract. In this study, we propose a dense, non-rigidly deformable, and real-time map fusion approach for actively controlled endoscopic capsule robot applications. The method combines magnetic and vision based localization, and makes use of frame-to-model fusion and model-to-model loop closure. The performance of the method is demonstrated using an ex-vivo porcine stomach model. Across four trajectories of varying speed and complexity, and across three cameras, the root mean square localization errors range from 0.42 to 1.92 cm, and the root mean square surface reconstruction errors range from 1.23 to 2.39 cm.