Tracking monocular camera pose and deformation for SLAM inside the human body
This addresses the problem of enabling SLAM in deformable human body environments for medical professionals, representing a novel method for a known bottleneck rather than an incremental improvement.
The paper tackles the problem of simultaneously tracking camera pose and 3D scene deformation in monocular SLAM for medical applications like endoscopy, achieving accuracy and robustness in simulated colonoscopies and successfully handling real-world challenges such as deformations and illumination changes.
Monocular SLAM in deformable scenes will open the way to multiple medical applications like computer-assisted navigation in endoscopy, automatic drug delivery or autonomous robotic surgery. In this paper we propose a novel method to simultaneously track the camera pose and the 3D scene deformation, without any assumption about environment topology or shape. The method uses an illumination-invariant photometric method to track image features and estimates camera motion and deformation combining reprojection error with spatial and temporal regularization of deformations. Our results in simulated colonoscopies show the method's accuracy and robustness in complex scenes under increasing levels of deformation. Our qualitative results in human colonoscopies from Endomapper dataset show that the method is able to successfully cope with the challenges of real endoscopies: deformations, low texture and strong illumination changes. We also compare with previous tracking methods in simpler scenarios from Hamlyn dataset where we obtain competitive performance, without needing any topological assumption.