On challenges of monocular pose estimation for endoluminal navigation

Accurate endoscope navigation is vital for precise sampling and drug delivery during endoluminal procedures. Current systems rely on sensorized endoscopes for real-time bronchoscope tracking, but they are costly and require specialised settings. Camera pose estimation offers an alternative for endoscope motion tracking and navigation. In this study, we assess state-of-the-art monocular pose estimation methods for endoluminal navigation. We analyze multiple datasets and diverse feature tracking frameworks, including SIFT, ORB, BRISK, FREAK, AKAZE, SuperPoint, LoFTR, and EndoSfMLearner. Our findings reveal that, while modern deep learning-based feature detectors like SuperPoint and LoFTR yield denser features compared to traditional methods, their overall quality and reliability remain suboptimal. This insight is crucial for understanding the limitations of current monocular odometry in endoluminal environments. We also emphasize the challenges in generalizing learned frameworks developed for other odometry applications to endoscopic applications, stressing the need for more publicly available datasets. To fill this gap, we introduce a clinical bronchoscopy dataset with ground truth camera pose data. This dataset serves as a realistic benchmark for improving feature-based odometry in surgical navigation and encourages tailored framework development for learned pose estimation in endoluminal interventions.